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Статті в журналах з теми "Localization density"

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Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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1

Gadre, Shridhar R., Sudhir A. Kulkarni, and Rajeev K. Pathak. "Density‐based electron localization function via nonlocal density approximation." Journal of Chemical Physics 98, no. 4 (February 15, 1993): 3574–76. http://dx.doi.org/10.1063/1.464082.

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2

Movaghar, B. "Localization and the density of states." Philosophical Magazine B 65, no. 5 (May 1992): 1097–108. http://dx.doi.org/10.1080/13642819208217923.

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3

Balan, Radu, Peter G. Casazza, Christopher Heil, and Zeph Landau. "Density, overcompleteness, and localization of frames." Electronic Research Announcements of the American Mathematical Society 12, no. 10 (July 7, 2006): 71–86. http://dx.doi.org/10.1090/s1079-6762-06-00163-6.

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4

Hutník, Ondrej, Egor A. Maximenko, and Anna Mišková. "Toeplitz Localization Operators: Spectral Functions Density." Complex Analysis and Operator Theory 10, no. 8 (May 20, 2016): 1757–74. http://dx.doi.org/10.1007/s11785-016-0564-1.

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5

Pilmé, Julien. "Electron localization function from density components." Journal of Computational Chemistry 38, no. 4 (November 17, 2016): 204–10. http://dx.doi.org/10.1002/jcc.24672.

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6

Schroer, Bert. "Area density of localization entropy: I. The case of wedge localization." Classical and Quantum Gravity 23, no. 17 (August 7, 2006): 5227–48. http://dx.doi.org/10.1088/0264-9381/23/17/008.

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7

Bouhdid, Badia, Wafa Akkari, and Sofien Gannouni. "Low Cost Recursive Localization scheme for High Density Wireless Sensor Networks." International Journal on Semantic Web and Information Systems 13, no. 3 (July 2017): 68–88. http://dx.doi.org/10.4018/ijswis.2017070104.

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Анотація:

While existing localization approaches mainly focus on enhancing the accuracy, particular attention has recently been given to reducing the localization algorithm implementation costs. To obtain a tradeoff between location accuracy and implementation cost, recursive localization approaches are being pursued as a cost-effective alternative to the more expensive localization approaches. In the recursive approach, localization information increases progressively as new nodes compute their positions and become themselves reference nodes. A strategy is then required to control and maintain the distribution of these new reference nodes. The lack of such a strategy leads, especially in high density networks, to wasted energy, important communication overhead and even impacts the localization accuracy. In this paper, the authors propose an efficient recursive localization approach that reduces the energy consumption, the execution time, and the communication overhead, yet it increases the localization accuracy through an adequate distribution of reference nodes within the network.

8

Suslov, Igor' M. "Density of states near the localization threshold." Uspekhi Fizicheskih Nauk 166, no. 8 (1996): 907. http://dx.doi.org/10.3367/ufnr.0166.199608x.0907.

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9

Marsh, Richard J., Karin Pfisterer, Pauline Bennett, Liisa M. Hirvonen, Mathias Gautel, Gareth E. Jones, and Susan Cox. "Artifact-free high-density localization microscopy analysis." Nature Methods 15, no. 9 (July 30, 2018): 689–92. http://dx.doi.org/10.1038/s41592-018-0072-5.

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10

Suslov, Igor' M. "Density of states near the localization threshold." Physics-Uspekhi 39, no. 8 (August 31, 1996): 848–49. http://dx.doi.org/10.1070/pu1996v039n08abeh001549.

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11

Schroer, Bert. "Addendum to ‘Area density of localization entropy: I. The case of wedge localization’." Classical and Quantum Gravity 24, no. 16 (July 31, 2007): 4239–49. http://dx.doi.org/10.1088/0264-9381/24/16/016.

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12

Gao, Ying, W. S. Zhao, C. Jing, and Wei Zheng Ren. "WSN Node Localization Algorithm Based on Adaptive Particle Swarm Optimization." Applied Mechanics and Materials 143-144 (December 2011): 302–6. http://dx.doi.org/10.4028/www.scientific.net/amm.143-144.302.

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Анотація:

In order to overcome shortcomings of existing range-free wireless sensor network (WSN) node localization methods such as huge computation volume and great effect of node density on localization precision, a WSN localization algorithm based on adaptive particle swarm optimization (APSO) was put forward in combination with particle swarm theory and DV-Hop algorithm. This algorithm improved localization precision by more than 20%, and the effect of node density on localization precision was significantly less than DV-Hop algorithm without any addition of hardware facilities and communication load.

13

Fang, Sheng En, Ricardo Perera, and Maria Consuelo Huerta. "Damage Localization Based on Power Spectral Density Analysis." Key Engineering Materials 347 (September 2007): 589–94. http://dx.doi.org/10.4028/www.scientific.net/kem.347.589.

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An environmental excitation having random characteristics may be more effective and cost-efficient than other excitation means for non-destructive damage identification purpose on most of the large-scale engineering structures under operation. In general, many existing damage indexes are constructed based on the modal properties derived firstly from the power spectral density (PSD) analysis of the structures under random excitation. However, the derivation procedures for the modal parameters usually introduce some extra errors into the indexes. This paper aims to propose a simple and feasible damage location index (DLI) constructed directly derived from the analysis results of the structural response PSD. The performance of DLI was verified using an aluminum beam with fixed ends and an experimental reinforced concrete (RC) beam under free boundary condition. Our results show that the damage location of the aluminum beam can be determined via the plot of DLI value by selecting the peaks with the amplitudes exceeding a predefined threshold value in both single- and multi-damaged scenarios. And the index may also predict the possible damage zones in the RC beam experimentally tested.

14

Miga, M. I., T. E. Kerner, and T. M. Darcey. "Source localization using a current-density minimization approach." IEEE Transactions on Biomedical Engineering 49, no. 7 (July 2002): 743–45. http://dx.doi.org/10.1109/tbme.2002.1010860.

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15

XiaoPing, Chen, and Shi BingRen. "Density Asymmetry and Marfe Localization in Tokamak Edge." Communications in Theoretical Physics 31, no. 4 (June 15, 1999): 625–30. http://dx.doi.org/10.1088/0253-6102/31/4/625.

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16

Balan, Radu, Peter G. Casazza, Christopher Heil, and Zeph Landau. "Density, Overcompleteness, and Localization of Frames. I. Theory." Journal of Fourier Analysis and Applications 12, no. 2 (April 2006): 105–43. http://dx.doi.org/10.1007/s00041-006-6022-0.

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17

Heer, C. V. "Orthopositronium localization in gases by density fluctuation scattering." Physics Letters A 110, no. 2 (July 1985): 99–102. http://dx.doi.org/10.1016/0375-9601(85)90328-7.

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18

Mukamel, Eran A., Hazen Babcock, and Xiaowei Zhuang. "Sparse Statistical Deconvolution for High-Density Localization Microscopy." Biophysical Journal 102, no. 3 (January 2012): 723a—724a. http://dx.doi.org/10.1016/j.bpj.2011.11.3927.

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19

Bahi, Jacques M., Abdallah Makhoul, and Ahmed Mostefaoui. "Localization and coverage for high density sensor networks." Computer Communications 31, no. 4 (March 2008): 770–81. http://dx.doi.org/10.1016/j.comcom.2007.10.022.

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20

Ying, Yue, Fuqing Zhang, and Jeffrey L. Anderson. "On the Selection of Localization Radius in Ensemble Filtering for Multiscale Quasigeostrophic Dynamics." Monthly Weather Review 146, no. 2 (February 2018): 543–60. http://dx.doi.org/10.1175/mwr-d-17-0336.1.

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Анотація:

Covariance localization remedies sampling errors due to limited ensemble size in ensemble data assimilation. Previous studies suggest that the optimal localization radius depends on ensemble size, observation density and accuracy, as well as the correlation length scale determined by model dynamics. A comprehensive localization theory for multiscale dynamical systems with varying observation density remains an active area of research. Using a two-layer quasigeostrophic (QG) model, this study systematically evaluates the sensitivity of the best Gaspari–Cohn localization radius to changes in model resolution, ensemble size, and observing networks. Numerical experiment results show that the best localization radius is smaller for smaller-scale components of a QG flow, indicating its scale dependency. The best localization radius is rather insensitive to changes in model resolution, as long as the key dynamical processes are reasonably well represented by the low-resolution model with inflation methods that account for representation errors. As ensemble size decreases, the best localization radius shifts to smaller values. However, for nonlocal correlations between an observation and state variables that peak at a certain distance, decreasing localization radii further within this distance does not reduce analysis errors. Increasing the density of an observing network has two effects that both reduce the best localization radius. First, the reduced observation error spectral variance further constrains prior ensembles at large scales. Less large-scale contribution results in a shorter overall correlation length, which favors a smaller localization radius. Second, a denser network provides more independent pieces of information, thus a smaller localization radius still allows the same number of observations to constrain each state variable.

21

Jiang, Tao, Xin Guo, Yongpeng Zhang, and Dongsheng Li. "Study and Quantitative Analysis of Mode Localization in Wind Turbine Blades." Journal of Marine Science and Engineering 12, no. 1 (December 27, 2023): 67. http://dx.doi.org/10.3390/jmse12010067.

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The study of damage mechanisms for wind turbine blades is important. Generally, modal localization tends to accelerate structural damage. This is a new approach to studying these damage mechanisms for wind turbine blades through modal localization theory. Therefore, this paper investigates whether modal localization phenomena exist in wind turbine blades, as well as the impact of different forms of detuning on modal localization. Based on perturbation theory, a mechanism for mode localization is described quantitatively using the degree of detuning, the degree of mode density, and the mode assurance criterion. A finite element model for wind turbine blades was established using ANSYS software (R15.0), and three detuning cases were simulated by changing the density, elastic modulus, and installation angles of the blades. Moreover, an improved mode localization factor is proposed to quantitatively evaluate the degree of mode localization in wind turbine blades. The numerical results indicate that the degree of modal localization increases with an increasing degree of detuning, but the increase in modal localization gradually slows. Finally, the detuning modal shape composition, which includes harmonic components, is analyzed. The results show that the closer the composition of the detuning modes is, the stronger the degree of mode localization.

22

Tjulin, A., A. I. Eriksson, and M. André. "Localization of wave fields in lower hybrid cavities." Annales Geophysicae 22, no. 8 (September 7, 2004): 2951–59. http://dx.doi.org/10.5194/angeo-22-2951-2004.

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Abstract. We investigate lower hybrid wave trapping in cylindrically symmetric density depletions in the electrostatic approximation. Our investigation is inspired by previous observations of such trapping by spacecraft in the auroral region at altitudes up to about 2000km, and the recent discovery of this phenomenon at altitudes above 20000km in the inner magnetosphere. No particular shape is assumed for the density depletion, which need not be strictly zero outside some value of the radial coordinate r. Important previously known properties concerning parabolic density depletions extending to finite r are shown to hold also for arbitrary shapes and infinite extent: for a given parallel wave number kz, modes below the ambient lower hybrid frequency fLH are trapped in the density depletion (in the sense that they are evanescent outside the cavity), have a discrete spectrum and rotate in a left-handed sense, while there is a continuous spectrum of freely propagating right-handed rotating modes above fLH. New results are such that even though the density depletion may go to zero slowly with increasing r, and thus be essentially infinite in extent, there is a maximum distance within which a trapped mode with given kz and azimuthal mode number m may propagate. Furthermore, we find that for any monotonic density cavity and given kz, there is a local relation between plasma density gradient and the lowest possible frequency that can be trapped. We combine our theoretical results with spacecraft observations to find an upper bound on kz. Our examples indicate that the length of the cavities is larger than the width by a factor of at least 100.

23

Wang, Zhaoyang, Xuebo Jin, Xiaoyi Wang, Jiping Xu, and Yuting Bai. "Hard Decision-Based Cooperative Localization for Wireless Sensor Networks." Sensors 19, no. 21 (October 27, 2019): 4665. http://dx.doi.org/10.3390/s19214665.

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Reliable and accurate localization of objects is essential for many applications in wireless networks. Especially for large-scale wireless sensor networks (WSNs), both low cost and high accuracy are targets of the localization technology. However, some range-free methods cannot be combined with a cooperative method, because these range-free methods are characterized by low accuracy of distance estimation. To solve this problem, we propose a hard decision-based cooperative localization method. For distance estimation, an exponential distance calibration formula is derived to estimate distance. In the cooperative phase, the cooperative method is optimized by outlier constraints from neighboring anchors. Simulations are conducted to verify the effectiveness of the proposed method. The results show that localization accuracy is improved in different scenarios, while high node density or anchor density contributes to the localization. For large-scale WSNs, the hard decision-based cooperative localization is proved to be effective.

24

Seijas, Luis E., Angel Lunar, Luis Rincón, and Rafael Almeida. "On the electron density localization in HF cyclic clusters." Journal of Computational Methods in Sciences and Engineering 17, no. 1 (March 9, 2017): 5–18. http://dx.doi.org/10.3233/jcm-160656.

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25

Rinawa, Motilal, Prashant Chauhan, Sintu Kumar, Manoj Kumar Singh, Hari Kumar Singh, Amit Sharma, and R. P. Sharma. "Field Localization and Density Cavitation in Low-Beta Plasmas." Laser and Particle Beams 2021 (November 28, 2021): 1–9. http://dx.doi.org/10.1155/2021/2891080.

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In the present paper, filamentous structure formation, associated turbulent spectrum, and density cavity formation phenomena have been investigated for low- β plasma β ≪ m e / m i applicable to the auroral region. A set of dimensionless equations governing the dynamics of three dimensionally propagating inertial Alfvén wave (3D-IAW) and perpendicularly propagating magnetosonic wave (PMSW) has been developed. Ponderomotive force due to 3D-IAW has been included in the dynamics of the PMSW. Numerical simulation has been performed to study the nonlinear coupling of these two waves. From the obtained results, we found that the field intensity localization takes place which may further lead to the additional dissipation/turbulence process for particle heating and acceleration in space plasma. The associated turbulent spectrum is obtained with scaling nearly k − 4.28 at smaller scales (in the dissipation range). Relevance of the obtained results with the observations reported by various spacecrafts such as Hawkeye and Heos 2 has been discussed. Also, density fluctuations (depletion) of ∼ 0.10 n 0 are calculated, which are consistent with the FAST spacecraft observation reported.

26

Zhao, Qing, Dong Wang, Yuehui Chen, and Xumi Qu. "Multisite protein subcellular localization prediction based on entropy density." Bio-Medical Materials and Engineering 26, s1 (August 17, 2015): S2003—S2009. http://dx.doi.org/10.3233/bme-151504.

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27

Sharma, S. C. "Positronium Localization in Spontaneous Density Fluctuations in Molecular Gases." Materials Science Forum 105-110 (January 1992): 451–58. http://dx.doi.org/10.4028/www.scientific.net/msf.105-110.451.

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28

Ovesný, Martin, Pavel Křížek, Zdeněk Švindrych, and Guy M. Hagen. "High density 3D localization microscopy using sparse support recovery." Optics Express 22, no. 25 (December 10, 2014): 31263. http://dx.doi.org/10.1364/oe.22.031263.

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29

Legant, Wesley R., Lin Shao, Jonathan B. Grimm, Timothy A. Brown, Daniel E. Milkie, Brian B. Avants, Luke D. Lavis, and Eric Betzig. "High-density three-dimensional localization microscopy across large volumes." Nature Methods 13, no. 4 (March 7, 2016): 359–65. http://dx.doi.org/10.1038/nmeth.3797.

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30

Svane, A., and O. Gunnarsson. "Localization in the self-interaction-corrected density-functional formalism." Physical Review B 37, no. 16 (June 1, 1988): 9919–22. http://dx.doi.org/10.1103/physrevb.37.9919.

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31

Song, Jasmine, Colin Davey, Catherine Poulsen, Phan Luu, Sergei Turovets, Erik Anderson, Kai Li, and Don Tucker. "EEG source localization: Sensor density and head surface coverage." Journal of Neuroscience Methods 256 (December 2015): 9–21. http://dx.doi.org/10.1016/j.jneumeth.2015.08.015.

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32

Tsirelson, Vladimir, and Adam Stash. "Determination of the electron localization function from electron density." Chemical Physics Letters 351, no. 1-2 (January 2002): 142–48. http://dx.doi.org/10.1016/s0009-2614(01)01361-6.

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33

Svane, A., and O. Gunnarson. "Localization in the self-interaction corrected density functional formalism." Journal de Chimie Physique 86 (1989): 823–30. http://dx.doi.org/10.1051/jcp/1989860823.

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34

Balan, Radu, Peter G. Casazza, Christopher Heil, and Zeph Landau. "Density, Overcompleteness, and Localization of Frames. II. Gabor Systems." Journal of Fourier Analysis and Applications 12, no. 3 (May 16, 2006): 307–44. http://dx.doi.org/10.1007/s00041-005-5035-4.

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35

Kong, Youngbae, Younggoo Kwon, Jeungwon Choi, Jonghwan Ko, and Gwitae Park. "Density adaptive localization for irregularly deployed wireless sensor networks." AEU - International Journal of Electronics and Communications 66, no. 12 (December 2012): 1026–31. http://dx.doi.org/10.1016/j.aeue.2012.05.006.

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36

Surj�n, P�ter R., J�nos Pipek, and B�la Paizs. "Localization maps by orbital partitioning of the electron density." Theoretica Chimica Acta 86, no. 5 (October 1993): 379–89. http://dx.doi.org/10.1007/bf01122430.

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37

Borghesani, A. F., and M. Santini. "Electron localization-delocalization transition in high-density neon gas." Physical Review A 45, no. 12 (June 1, 1992): 8803–10. http://dx.doi.org/10.1103/physreva.45.8803.

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38

Ikuta, Togo, Yasuhito Kobayashi, and Kaname Kawajiri. "Cell Density Regulates Intracellular Localization of Aryl Hydrocarbon Receptor." Journal of Biological Chemistry 279, no. 18 (February 25, 2004): 19209–16. http://dx.doi.org/10.1074/jbc.m310492200.

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39

Mahajan, Swadesh M., and Nana L. Shatashvili. "Wave localization and density bunching in pair ion plasmas." Physics of Plasmas 15, no. 10 (October 2008): 100701. http://dx.doi.org/10.1063/1.3005382.

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40

DiGiuseppe, Nicholas, and James A. Jones. "Fault density, fault types, and spectra-based fault localization." Empirical Software Engineering 20, no. 4 (March 18, 2014): 928–67. http://dx.doi.org/10.1007/s10664-014-9304-1.

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41

Andrade, José E., and Ronaldo I. Borja. "Capturing strain localization in dense sands with random density." International Journal for Numerical Methods in Engineering 67, no. 11 (2006): 1531–64. http://dx.doi.org/10.1002/nme.1673.

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42

Sedelnikova, Olga V., Lyubov G. Bulusheva та Alexander V. Okotrub. "Localization of π-electron density in twisted bilayer graphene". physica status solidi (RRL) - Rapid Research Letters 11, № 2 (14 грудня 2016): 1600367. http://dx.doi.org/10.1002/pssr.201600367.

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43

Bera, Soumya, Thomas Martynec, Henning Schomerus, Fabian Heidrich-Meisner, and Jens H. Bardarson. "One-particle density matrix characterization of many-body localization." Annalen der Physik 529, no. 7 (February 6, 2017): 1600356. http://dx.doi.org/10.1002/andp.201600356.

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44

Wang, Yina, Tingwei Quan, Shaoqun Zeng, and Zhen-Li Huang. "PALMER: a method capable of parallel localization of multiple emitters for high-density localization microscopy." Optics Express 20, no. 14 (June 29, 2012): 16039. http://dx.doi.org/10.1364/oe.20.016039.

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45

Santana-Carrillo, R., Roberto de J. León-Montiel, Guo-Hua Sun, and Shi-Hai Dong. "Quantum Information Entropy for Another Class of New Proposed Hyperbolic Potentials." Entropy 25, no. 9 (September 5, 2023): 1296. http://dx.doi.org/10.3390/e25091296.

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Анотація:

In this work, we investigate the Shannon entropy of four recently proposed hyperbolic potentials through studying position and momentum entropies. Our analysis reveals that the wave functions of the single-well potentials U0,3 exhibit greater localization compared to the double-well potentials U1,2. This difference in localization arises from the depths of the single- and double-well potentials. Specifically, we observe that the position entropy density shows higher localization for the single-well potentials, while their momentum probability density becomes more delocalized. Conversely, the double-well potentials demonstrate the opposite behavior, with position entropy density being less localized and momentum probability density showing increased localization. Notably, our study also involves examining the Bialynicki–Birula and Mycielski (BBM) inequality, where we find that the Shannon entropies still satisfy this inequality for varying depths u¯. An intriguing observation is that the sum of position and momentum entropies increases with the variable u¯ for potentials U1,2,3, while for U0, the sum decreases with u¯. Additionally, the sum of the cases U0 and U3 almost remains constant within the relative value 0.01 as u¯ increases. Our study provides valuable insights into the Shannon entropy behavior for these hyperbolic potentials, shedding light on their localization characteristics and their relation to the potential depths. Finally, we extend our analysis to the Fisher entropy F¯x and find that it increases with the depth u¯ of the potential wells but F¯p decreases with the depth.

46

González-García, Luis Antonio, Héctor Alva-Sánchez, and Rosario Paredes. "Localization in Two-Dimensional Quasicrystalline Lattices." Entropy 24, no. 11 (November 10, 2022): 1628. http://dx.doi.org/10.3390/e24111628.

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We investigate the emergence of localization in a weakly interacting Bose gas confined in quasicrystalline lattices with three different rotational symmetries: five, eight, and twelve. The analysis, performed at a mean field level and from which localization is detected, relies on the study of two observables: the inverse participation ratio (IPR) and the Shannon entropy in the coordinate space. Those physical quantities were determined from a robust statistical study for the stationary density profiles of the interacting condensate. Localization was identified for each lattice type as a function of the potential depth. Our analysis revealed a range of the potential depths for which the condensate density becomes localized, from partially at random lattice sites to fully in a single site. We found that localization in the case of five-fold rotational symmetry appears for (6ER,9ER), while it occurs in the interval (12ER,15ER) for octagonal and dodecagonal symmetries.

47

Torres-Vega, Juan J., Diego R. Alcoba, Ofelia B. Oña, Alejandro Vásquez-Espinal, Rodrigo Báez-Grez, Luis Lain, Alicia Torre, Víctor García, and William Tiznado. "Analysis of Local and Global Aromaticity in Si3C5 and Si4C8 Clusters. Aromatic Species Containing Planar Tetracoordinate Carbon." Chemistry 3, no. 4 (September 25, 2021): 1101–12. http://dx.doi.org/10.3390/chemistry3040080.

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The minimum energy structures of the Si3C5 and Si4C8 clusters are planar and contain planar tetracoordinate carbons (ptCs). These species have been classified, qualitatively, as global (π) and local (σ) aromatics according to the adaptive natural density partitioning (AdNDP) method, which is an orbital localization method. This work evaluates these species’ aromaticity, focusing on confirming and quantifying their global and local aromatic character. For this purpose, we use an orbital localization method based on the partitioning of the molecular space according to the topology of the electronic localization function (LOC-ELF). In addition, the magnetically induced current density is analyzed. The LOC-ELF-based analysis coincides with the AdNDP study (double aromaticity, global, and local). Moreover, the current density analysis detects global and local ring currents. The strength of the global and local current circuit is significant, involving 4n + 2 π- and σ-electrons, respectively. The latter implicates the Si-ptC-Si fragment, which would be related to the 3c-2e σ-bond detected by the orbital localization methods in this fragment.

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Lu, Kezhong, Xiaohua Xiang, Dian Zhang, Rui Mao, and Yuhong Feng. "Localization Algorithm Based on Maximum a Posteriori in Wireless Sensor Networks." International Journal of Distributed Sensor Networks 8, no. 1 (December 15, 2011): 260302. http://dx.doi.org/10.1155/2012/260302.

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Many applications and protocols in wireless sensor networks need to know the locations of sensor nodes. A low-cost method to localize sensor nodes is to use received signal strength indication (RSSI) ranging technique together with the least-squares trilateration. However, the average localization error of this method is large due to the large ranging error of RSSI ranging technique. To reduce the average localization error, we propose a localization algorithm based on maximum a posteriori. This algorithm uses the Baye's formula to deduce the probability density of each sensor node's distribution in the target region from RSSI values. Then, each sensor node takes the point with the maximum probability density as its estimated location. Through simulation studies, we show that this algorithm outperforms the least-squares trilateration with respect to the average localization error.

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Bathgate, R. A. D., and C. Sernia. "Characterization and localization of oxytocin receptors in the rat testis." Journal of Endocrinology 141, no. 2 (May 1994): 343–52. http://dx.doi.org/10.1677/joe.0.1410343.

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Abstract In this study oxytocin (OT) receptors have been characterized and localized in the testis of the rat using the radioiodinated OT receptor antagonist 125I-labelled d(CH2)5 [Tyr(Me)2,Thr4,Tyr9-NH2]-vasotocin (OTA). Receptor density and localization have been compared with the rat testis arginine vasopressin (AVP) receptor using the radioiodinated AVP V1a receptor antagonist 125I-labelled d(CH2)5Sar7-AVP and the radioiodinated linear AVP V1a antagonist 125I-labelled [(C6H5-CH2CO)-O-methyl-d-Tyr-Phe-Gln-Asn-Arg-Pro-Arg-Tyr-NH2]. 125I-labelled OTA bound with high affinity to membrane fractions of the rat testis (Ka = 13·8 ± 1·25 litres/nmol), mammary tissue (Ka=20·3± 4·36 litres/nmol) and uterus (Ka=27·8±0·74 litres/nmol). Competition studies with various OT and AVP receptor agonists and antagonists confirmed that the binding was to OT receptors. AVP receptors in the testis were found to be identical to AVP V1a receptors in the liver. The AVP receptor density in the testis was much higher than the OT receptor density (109 ±12·3 vs 5·2 ±0·79 (mean ± s.e.m.) fmol/mg protein). Autoradiographical localization showed that both OT and AVP receptors were present in the interstitial spaces in the testis consistent with binding to Leydig cells. AVP receptors were also localized on the epithelial surfaces of the seminiferous tubules and on testicular blood vessels. This study has, for the first time, found OT receptors in the testis of the rat which have similar ligand-binding characteristics to mammary and uterine OT receptors. The receptor localizations are consistent with binding to Leydig cells. Journal of Endocrinology (1994) 141, 343–352

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Ferreira, David, Richard Souza, and Celso Carvalho. "QA-kNN: Indoor Localization Based on Quartile Analysis and the kNN Classifier for Wireless Networks." Sensors 20, no. 17 (August 21, 2020): 4714. http://dx.doi.org/10.3390/s20174714.

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Considering the variation of the received signal strength indicator (RSSI) in wireless networks, the objective of this study is to investigate and propose a method of indoor localization in order to improve the accuracy of localization that is compromised by RSSI variation. For this, quartile analysis is used for data pre-processing and the k-nearest neighbors (kNN) classifier is used for localization. In addition to the tests in a real environment, simulations were performed, varying many parameters related to the proposed method and the environment. In the real environment with reference points of 1.284 density per unit area (RPs/m2), the method presents zero-mean error in the localization in test points (TPs) coinciding with the RPs. In the simulated environment with a density of 0.327 RPs/m2, a mean error of 0.490 m for the localization of random TPs was achieved. These results are important contributions and allow us to conclude that the method is promising for locating objects in indoor environments.