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Journal articles on the topic 'Distance'

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Author: Grafiati

Published: 4 June 2021

Last updated: 28 July 2024

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1

Blanco-Mesa, F., and J. M. Merigó. "BONFERRONI DISTANCES WITH HYBRID WEIGHTED DISTANCE AND IMMEDIATE WEIGHTED DISTANCE." FUZZY ECONOMIC REVIEW 22, no. 02 (2017): 2274. http://dx.doi.org/10.25102/fer.2017.02.02.

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2

Segger, Martin. "Canada: great distances require distance training…" Museum International 39, no. 4 (December 1987): 244–48. http://dx.doi.org/10.1111/j.1468-0033.1987.tb00702.x.

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3

McDiarmid, Colin. "Frequency-distance constraints with large distances." Discrete Mathematics 223, no. 1-3 (August 2000): 227–51. http://dx.doi.org/10.1016/s0012-365x(00)00016-9.

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4

Servyugin, N. N., A. D. Lagoda, V. V. Yudin, and A. V. Kuznetsov. "Laser Distance Meter for Short Distances." Measurement Techniques 48, no. 7 (July 2005): 654–56. http://dx.doi.org/10.1007/s11018-005-0199-5.

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5

Tian, Fang, and Jun-Ming Xu. "Average distances and distance domination numbers." Discrete Applied Mathematics 157, no. 5 (March 2009): 1113–27. http://dx.doi.org/10.1016/j.dam.2008.03.024.

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6

HARWIT, M. "Cosmic Distances: The Cosmological Distance Ladder." Science 230, no. 4722 (October 11, 1985): 163. http://dx.doi.org/10.1126/science.230.4722.163.

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7

Wang, Shixiang, and Minyuan Zhao. "The Tale of Two Distances: Technological Distance, Geographic Distance, and Multi-locational Firms." Academy of Management Proceedings 2018, no. 1 (August 2018): 13737. http://dx.doi.org/10.5465/ambpp.2018.13737abstract.

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8

Damti, Yanir, Ilan Gronau, Shlomo Moran, and Irad Yavneh. "Comparing evolutionary distances via adaptive distance functions." Journal of Theoretical Biology 440 (March 2018): 88–99. http://dx.doi.org/10.1016/j.jtbi.2017.12.022.

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9

Zaks, Joseph. "Uniform distances in rational unit-distance graphs." Discrete Mathematics 109, no. 1-3 (November 1992): 307–11. http://dx.doi.org/10.1016/0012-365x(92)90301-u.

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10

VAN DEN BERGH, S. "Fixing Extragalactic Distances: Supernovae as Distance Indicators." Science 229, no. 4713 (August 9, 1985): 548. http://dx.doi.org/10.1126/science.229.4713.548.

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11

Randic, Milan, Alexander F. Kleiner, and Luz M. De Alba. "Distance/Distance Matrixes." Journal of Chemical Information and Modeling 34, no. 2 (March 1, 1994): 277–86. http://dx.doi.org/10.1021/ci00018a008.

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12

Wang, Shixiang, and Minyuan Zhao. "A tale of two distances: a study of technological distance, geographic distance and multilocation firms." Journal of Economic Geography 18, no. 5 (August 8, 2018): 1091–120. http://dx.doi.org/10.1093/jeg/lby042.

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13

Normand, Nicolas, Robin Strand, Pierre Evenou, and Aurore Arlicot. "AStreaming Distance Transform Algorithm for Neighborhood-Sequence Distances." Image Processing On Line 4 (September 1, 2014): 196–203. http://dx.doi.org/10.5201/ipol.2014.68.

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14

Park, Chanseok, Ayanendranath Basu, and Srabashi Basu. "Robust minimum distance inference based on combined distances." Communications in Statistics - Simulation and Computation 24, no. 3 (January 1995): 653–73. http://dx.doi.org/10.1080/03610919508813265.

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15

Hajdu, András, Lajos Hajdu, and Robert Tijdeman. "Approximation of the Euclidean Distance by Chamfer Distances." Acta Cybernetica 20, no. 3 (2012): 399–417. http://dx.doi.org/10.14232/actacyb.20.3.2012.3.

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16

Cubukcu, K. Mert, and Hatcha Taha. "Are Euclidean Distance and Network Distance Related ?" Environment-Behaviour Proceedings Journal 1, no. 4 (August 7, 2016): 167. http://dx.doi.org/10.21834/e-bpj.v1i4.137.

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Although spatial distance is a very important concept for a wide variety of disciplines including social, natural, and information sciences, the methods used to measure spatial distance are not directly expressed and fully explained. In this study, we calculate and compare Euclidean distances and network distances for 10 randomly selected European cities. On the contrary to the findings reported in past research, we find that there is not a global straight forward relation between the Euclidian distance and network distance.© 2016. The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.Keywords: Euclidean distance; network distance; network analysis

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17

Maglio, Sam J., Yaacov Trope, and Nira Liberman. "Distance from a distance: Psychological distance reduces sensitivity to any further psychological distance." Journal of Experimental Psychology: General 142, no. 3 (2013): 644–57. http://dx.doi.org/10.1037/a0030258.

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18

Godsil, Chris D., and John Shawe-Taylor. "Distance-regularised graphs are distance-regular or distance-biregular." Journal of Combinatorial Theory, Series B 43, no. 1 (August 1987): 14–24. http://dx.doi.org/10.1016/0095-8956(87)90027-x.

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19

Qureshi, Mohammad A., Ho-Ling Hwang, and Shih-Miao Chin. "Comparison of Distance Estimates for Commodity Flow Survey: Great Circle Distances Versus Network-Based Distances." Transportation Research Record: Journal of the Transportation Research Board 1804, no. 1 (January 2002): 212–16. http://dx.doi.org/10.3141/1804-28.

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A study was conducted to compare distance estimates derived from great circle distances (GCD) with distance estimates derived from a network-based model. The study used a sample of shipments from the 1993 commodity flow survey (CFS). For each shipment in the sample, the distance from the zip code of origin to the zip code of destination was calculated by using the Oak Ridge National Laboratories National Highway Network and assuming that the minimum impedance path was utilized. For each of these origin–destination pairs, the GCD and several variations of the GCD also were estimated. Finally, the network-based estimates and the GCD-based estimates were statistically compared. As expected, distance estimates based on GCD were found to be different from network-based estimates. However, applying a constant circuity factor of 1.22 or using variable circuity factors based on distance category did not result in a statistical bias in these distance estimates. Examination of distance estimates at the level of origin–destination pair revealed that distance estimates could vary as much as 75%. A comparison of published values for the 1997 CFS with values derived from GCD-based distance estimates shows that approximately 5% to 35% of the GCD-based values for the 1997 CFS would fall outside a two standard error interval. Although GCD-based estimates, under some conditions, may produce unbiased estimates of the mean distance, this does not eliminate the need for network-based estimates.

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20

Kurspahić Mujčić, Amira, and Amra Mujčić. "SOCIJALNA DISTANCA VEĆINSKE POPULACIJE PREMA OSOBAMA SA INTELEKTUALNIM POTEŠKOĆAMA." Multidisciplinarni Pristupi u Edukaciji i Rehabilitaciji 2, no. 2 (August 15, 2020): 75–85. http://dx.doi.org/10.59519/mper2006.

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Uvod Socijalna distanca često se koristi kao pokazatelj diskriminacije i odnosi se na sklonost ljudi da izbjegavaju kontakte sa stigmatiziranom populacijom, uključujući osobe sa intelektualnim poteškoćama. Cilj Istražiti razinu socijalne distance opće populacije prema pojedincima sa intelektualnim poteškoćama i istražiti značajne korelate. Metod Ova studija presjeka je provedena u ambulantama porodične medicine JU Dom zdravlja Kantona Sarajevo. U istraživanju je učestvovalo 240 ispitanika. Kao instrument istraživanja korišten je upitnik o općim socio-demografskim karakteristikama ispitanika i skala socijalne distance (modificirana Bogardus-ova skala). Rezultati Vrijednost medijane skale socijalne distance je iznosila 17 (minimum, 6; maximum, 30). Najintimniji društveni odnosi (brak) nalaze se na vrhu ljestvice. Muškarci su zauzele stavove koji su odražavali veću socijalnu distancu od žena (p = 0,016). Stariji sudionici iskazali su veću socijalnu distancu od mlađih, ali ne statistički značajno (p = 0,323). Zaključak Danas kada se velika pažnja poklanja uključivanju u zajednicu svih osobe sa intelektualnim poteškoćama, stigmatizirajući stavovi većinske populacije bi mogli predstavljati barijeru za efikasno provođenje socijalne inkluzije.

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21

Barnes, Thomas G., Thomas J. Moffett, and Wolfgang P. Gieren. "A Preliminary Distance to the Small Magellanic Cloud by the Surface Brightness Technique." International Astronomical Union Colloquium 139 (1993): 93. http://dx.doi.org/10.1017/s0252921100116975.

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AbstractWe present a new distance determination to the Small Magellanic Cloud from the surface brightness technique applied to the Cepheid variable HV 829. Although this is a preliminary distance based on only one star, it illustrates the power of the surface brightness technique to extragalactic Cepheid distances, it develops the technique which we will apply to additional SMC and LMC Cepheids, and the distance is of intrinsic interest because of the current controvery concerning distances for the Magellanic Clouds.For HV 829 itself we obtain a distance modulus of 18.91 ± 0.20 mag. From other evidence we infer that HV 829 is slightly in front of the SMC centroid distance. Correcting to the SMC centroid yields a distance to the SMC of 19.05 ± 0.20 mag. We stress that this distance modulus is fully independent of any other distance modulus for the SMC, including those based upon Cepheids. Even so, our result agrees more closely with other, independent Cepheid distances than with RR Lyrae distances and main sequence fitting distances.

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22

Park, K. T. "Short distance ultrasonic distance meter." Journal of the Acoustical Society of America 104, no. 1 (July 1998): 23. http://dx.doi.org/10.1121/1.424004.

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23

Petre, Marian. "DISTANCE EDUCATIONDoctorates at a distance." ACM Inroads 1, no. 2 (June 2010): 19–20. http://dx.doi.org/10.1145/1805724.1805731.

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24

Guzmán Naranjo, Matías, and Gerhard Jäger. "Euclide, the crow, the wolf and the pedestrian: distance metrics for linguistic typology." Open Research Europe 3 (June 21, 2023): 104. http://dx.doi.org/10.12688/openreseurope.16141.1.

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It is common for people working on linguistic geography, language contact and typology to make use of some type of distance metric between lects. However, most work so far has either used Euclidean distances, or geodesic distance, both of which do not represent the real separation between communities very accurately. This paper presents two datasets: one on walking distances and one on topographic distances between over 8700 lects across all macro-areas. We calculated walking distances using Open Street Maps data, and topographic distances using digital elevation data. We evaluate these distances. We evaluate these distance metrics on three case studies and show that topographic distance tends to outperform the other distance metrics, but geodesic distances can be used as an adequate approximation in some cases.

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25

Yadav, Dipendra Prasad, Nand Kishor Kumar, and Suresh Kumar Sahani. "Distance Metrics for Machine Learning and it's Relation with Other Distances." Mikailalsys Journal of Mathematics and Statistics 1, no. 1 (October 27, 2023): 15–23. http://dx.doi.org/10.58578/mjms.v1i1.1990.

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In machine learning, distance metrics play a crucial role in measuring the degree of dissimilarity among data points. When creating and optimizing machine learning models, data scientists and machine learning practitioners can make more informed choices by understanding the features of popular distance metrics and their relationships. The effectiveness and interpretability of the model's output can be greatly influenced by selecting the appropriate distance metric. We explain distance metrics and their relevance in machine learning with various examples of metrics, including Minkowski distance, Manhattan distance, Max Metric for R^n, Taxicab distance, Relative distance, and Hamming distance.

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26

Fiol, M. A. "Pseudo-distance-regularized graphs are distance-regular or distance-biregular." Linear Algebra and its Applications 437, no. 12 (December 2012): 2973–77. http://dx.doi.org/10.1016/j.laa.2012.07.019.

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27

Trautmann, Stefan T. "Distance from a distance: the robustness of psychological distance effects." Theory and Decision 87, no. 1 (March 15, 2019): 1–15. http://dx.doi.org/10.1007/s11238-019-09696-6.

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28

Luong, A., and M. E. Thompson. "Minimum-distance methods based on quadratic distances for transforms." Canadian Journal of Statistics 15, no. 3 (September 1987): 239–51. http://dx.doi.org/10.2307/3314914.

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29

Phillips, Mark Salber. "Distance and Distances in our View of the Past." Journal of the Canadian Historical Association 26, no. 2 (2015): 32. http://dx.doi.org/10.7202/1037221ar.

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30

Jung, Soon-Mo. "Inequalities for distances between points and distance preserving mappings." Nonlinear Analysis: Theory, Methods & Applications 62, no. 4 (August 2005): 675–81. http://dx.doi.org/10.1016/j.na.2005.04.003.

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31

Elen, Abdullah, and Emre Avuçlu. "Standardized Variable Distances: A distance-based machine learning method." Applied Soft Computing 98 (January 2021): 106855. http://dx.doi.org/10.1016/j.asoc.2020.106855.

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32

Crippen, Gordon M. "An alternative approach to distance geometry using L∞ distances." Discrete Applied Mathematics 197 (December 2015): 20–26. http://dx.doi.org/10.1016/j.dam.2014.08.019.

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33

Dron, Jon. "Technology, Teaching, and the Many Distances of Distance Learning." Journal of Open, Flexible and Distance Learning 26, no. 2 (February 14, 2023): 7–17. http://dx.doi.org/10.61468/jofdl.v26i2.557.

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The “distance” in “distance learning”, however it is defined, normally refers to a gap between a learner and their teacher(s), typically in a formal context. In this paper I take a slightly different view. The paper begins with an argument that teaching is fundamentally a technological process. It is, though, a vastly complex, massively distributed technology in which the most important parts are enacted idiosyncratically by vast numbers of people, both present and distant in time and space, who not only use technologies but also participate creatively in their enactment. Through the techniques we use we are co-participants in not just technologies but the learning of ourselves and others, and hence in the collective intelligence of those around us and, ultimately, that of our species. We are all teachers. There is therefore not one distance between learner and teacher in any act of deliberate learning— but many. I go on to speculate on alternative ways of understanding distance in terms of the physical, temporal, structural, agency, social, emotional, cognitive, cultural, pedagogical, and technological gaps that may exist between learners and their many teachers. And I conclude with some broad suggestions about ways to reduce these many distances

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34

Newman, Karen. "Cross-national Distance: Concepts, Measures and Relationships." Journal of International Business and Economy 13, no. 2 (December 1, 2012): 37–59. http://dx.doi.org/10.51240/jibe.2012.2.2.

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Cross-national distances between national cultures and national institutions have been studied extensively in the last two decades, particularly with respect to their effects on the conduct of international business. Yet varying levels of analysis, inconsistent definitions, and different operationalizations of cross-national distances inhibit theoretical and empirical advances. Three approaches to non-geographic cross-national distance permeate the literature: psychic distance, national cultural distance, and institutional distance. The meaning of psychic distance has become muddied by evolving operationalizations, from objective indicators to individual perceptions. National cultural distance has been confused with both psychic distance and institutional distance. Various and inconsistent institutional arrangements and business practices are used as measures of institutional distance. This article reviews overlaps, inconsistencies, and ambiguities in the definitions and measurements of psychic, national cultural and institutional distance; suggests a way to rationalize the three constructs; and offers two competing models to explain the role of all three distances in international business decisions.

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35

Crabtree, Darryl A., and Mary P. Crabtree. "Transfer of Sensory-Integration Training." Perceptual and Motor Skills 64, no. 2 (April 1987): 643–49. http://dx.doi.org/10.2466/pms.1987.64.2.643.

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96 university students participated in one of six treatments to assess whether training to make cross-modal or intramodal matches of one linear-movement distance would transfer to other distances. Three groups received unimodal training (vision, audition, or kinesthesis) and three groups received multimodal Training (all combinations of vision, audition, and kinesthesis) to make kinesthetic matches of one common linear-movement distance. All subjects were subsequently tested on kinesthetic matches of novel distances both shorter and longer than the training distance. A multivariate analysis of variance and Bonferroni contrasts indicated that cross-modal matching was enhanced by multimodal experience but only for distances shorter than the training distance. It was speculated that transferring to distances shorter than a training distance might involve processes different from those used to transfer to distances longer than the training distance.

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36

Stojanovic, Radmila, and Suncica Zdravkovic. "Symbolic distance: Unfamiliar versus familiar space." Psihologija 40, no. 1 (2007): 93–110. http://dx.doi.org/10.2298/psi0701093s.

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The symbolic distance effect was investigated using both realistic distances and distances represented on the map. The influence of professional orientation and sex on mental visualization was measured. The results showed that an increase of distance leads to an increase in reaction time. The slope for realistic distances was steeper. Male subjects always had longer reaction times, although the effect differs for the two types of distances. Professional orientation did not play a role. The obtained relation between reaction time and distance is a confirmation of theories proposing that mental representations encompass structure and metric characteristics. The confirmed role of the effect of symbolic distance additionally supports Kosslyn?s theory: there is a linear relation between the time and distance.

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37

Bugislaus, A. E. "Genetic analysis of distance-dependent racing performances in German Thoroughbreds." Archives Animal Breeding 53, no. 6 (October 10, 2010): 629–38. http://dx.doi.org/10.5194/aab-53-629-2010.

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Abstract. The objective of this study was to develop a new multivariate statistical model for genetic estimation of distance-dependent racing performances in German Thoroughbreds. Analysed performance traits were »square root of distance to first placed horse in races over sprint distances (until 1 400 m)«, »square root of distance to first placed horse in races over mile distances (from 1 401 m to 1 900 m)« and »square root of distance to first placed horse in races over long distances (over 1 900 m)«. These traits were found to be influenced by the carried weight, which was determined by the horses’ earlier performance. Therefore, new traits were developed based on random regression models, which were independent from the carried weights. Genetic parameters were first evaluated for these new created traits »new distance to first placed horse in races over sprint distances« (h²=0.088), »new distance to first placed horse in races over mile distances« (h²=0.081) and »new distance to first placed horse in races over long distances« (h²=0.137) using a multivariate animal model. Genetic correlations between these traits were high, but differed from rg=1. A further heritability was estimated for the distance-independent trait »new distance to first placed horse in races over all distances« (h²=0.101) applying a univariate animal model with a fixed distance effect. These two different models were compared by two criteria. The ranking of breeding values for the distance-independent trait (estimated with the univariate model) was first correlated with each of the rankings of breeding values for the three distance-dependent traits (estimated with the multivariate model). Correlations varied from r=0.668 to r=0.813. The second criterion for comparison was the percentage of incorrectly selected raced stallions by breeding values estimated with the univariate model. Between 47.4 % and 69.7 % of stallions were incorrectly selected. The use of a total selection index including breeding values of the three distance-dependent traits with suitable weightings was recommended as a possible future selection criterion.

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38

Afghani, Said Al, and Widhera Yoza Mahana Putra. "Clustering with Euclidean Distance, Manhattan - Distance, Mahalanobis - Euclidean Distance, and Chebyshev Distance with Their Accuracy." Indonesian Journal of Statistics and Its Applications 5, no. 2 (June 30, 2021): 369–76. http://dx.doi.org/10.29244/ijsa.v5i2p369-376.

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There are several algorithms to solve many problems in grouping data. Grouping data is also known as clusterization, clustering takes advantage to solve some problems especially in business. In this note, we will modify the clustering algorithm based on distance principle which background of K-means algorithm (Euclidean distance). Manhattan, Mahalanobis-Euclidean, and Chebyshev distance will be used to modify the K-means algorithm. We compare the clustered result related to their accuracy, we got Mahalanobis - Euclidean distance gives the best accuracy on our experiment data, and some results are also given in this note.

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39

Navarro, Silvana G., Romano L. M. Corradi, and Antonio Mampaso. "Distance determination to PNe using the extinction-distance method." Proceedings of the International Astronomical Union 7, S283 (July 2011): 460–61. http://dx.doi.org/10.1017/s1743921312011921.

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AbstractWe present individual distances to three PNe: NGC 6537, NGC 6781 and NGC 7027, determined by the extinction-distance method. These objects are part of a larger sample (35) of PNe that we observed at ORM. In order to apply this method, and to obtain accurate distances, we determined the spectral type of 40 to 60 stars in the line of sight of each PNe. This implied the necessity of classifying few thousands of stellar spectra with S/N ratio between 10 and 60. To solve such need we developed an ANN system to perform automatic spectral classification which could classify spectra with S/N ratio as low as 20 with an accuracy better than 2 spectral subtypes. In this poster we compare the accuracy of such distances with previous distance determinations using other methods. We conclude that it is possible to use this method to obtain the distance of a large number of PNe with better precision.

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40

Golse, Bernard. "À distance du présentiel, la présence du distanciel." Le Carnet PSY N°237, no. 7 (2020): 1. http://dx.doi.org/10.3917/lcp.237.0001.

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41

Qin, Xianxiang, Yanning Zhang, Ying Li, Yinglei Cheng, Wangsheng Yu, Peng Wang, and Huanxin Zou. "Distance Measures of Polarimetric SAR Image Data: A Survey." Remote Sensing 14, no. 22 (November 19, 2022): 5873. http://dx.doi.org/10.3390/rs14225873.

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Distance measure plays a critical role in various applications of polarimetric synthetic aperture radar (PolSAR) image data. In recent decades, plenty of distance measures have been developed for PolSAR image data from different perspectives, which, however, have not been well analyzed and summarized. In order to make better use of these distance measures in algorithm design, this paper provides a systematic survey of them and analyzes their relations in detail. We divide these distance measures into five main categories (i.e., the norm distances, geodesic distances, maximum likelihood (ML) distances, generalized likelihood ratio test (GLRT) distances, stochastics distances) and two other categories (i.e., the inter-patch distances and those based on metric learning). Furthermore, we analyze the relations between different distance measures and visualize them with graphs to make them clearer. Moreover, some properties of the main distance measures are discussed, and some advice for choosing distances in algorithm design is also provided. This survey can serve as a reference for researchers in PolSAR image processing, analysis, and related fields.

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42

Bühligen, U., T. Neumuth, and S. Schumann. "Distance Measures for Surgical Process Models." Methods of Information in Medicine 52, no. 05 (2013): 422–31. http://dx.doi.org/10.3414/me12-01-0111.

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SummaryBackground: The development of new resources, such as surgical techniques and approaches, results in continuous modification of surgery. To assess these modifications, it is necessary to use measures that quantify the impact of resources on surgical processes.Objectives: The objective of this work is to introduce and evaluate distance measurements that are able to represent differences in the courses of surgical interventions as processes.Methods: Hence, we present four different distance measures for surgical processes: the Jaccard distance, Levenshtein distance, Adjacency distance, and Graph matching distance. These measures are formally introduced and evaluated by applying them to clinical data sets from laparoscopic training in pediatric surgery.Results: We analyzed the distances of 450 surgical processes using these four measures with a focus on the difference in surgical processes performed by novices and by experienced surgeons. The Levenshtein and Adjacency distances were best suited to measure distances between surgical processes.Conclusion: The measurement of distances between surgical processes is necessary to estimate the benefit of new surgical techniques and strategies.

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43

Janpathompong, Shusak, Nij Tontisirin, and Akinobu Murakami. "How far people walk during lunchtime: Comparing actual and perceived walking distances in the Central Business District of Bangkok, Thailand." Nakhara : Journal of Environmental Design and Planning 21, no. 3 (December 30, 2022): 227. http://dx.doi.org/10.54028/nj202221227.

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Walking has long been considered a significant factor in wellness. Previous studies have indicated 400-500 meters as a standard of the “acceptable walking distance” as compared to driving or other forms of transportation. However, “acceptable walking distance” and a “distance that one chooses to walk” are two different matters. More importantly, the distance people perceive for a trip can be one of the deciding factors in determining whether they are willing to walk, decide not to walk, or shift to other transportation modes. This research aims to define the optimal walking distances of office workers by measuring the discrepancy between their estimated and actual walking distances in the Central Business District of Bangkok during lunchtime, their most extended break during the day. The descriptive statistic shows that the average walking distance is 302.39 meters, but most workers walked shorter distances since the median distance is 211.66 meters. Nevertheless, these office workers, on the whole, significantly miscalculated their walking distances, with an average over-estimation of 191.45 meters. Regression analysis shows that, when walking for distances up to 380 meters, most workers overestimated the distance they walked, but that, when walking beyond that distance (that is, 380 meters), they estimated correctly, which indicates the maximum actual length they choose to walk.

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44

Gallacher, Kaila. "Distance." in:cite journal 4 (August 13, 2021): 49–51. http://dx.doi.org/10.33137/incite.4.37158.

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45

Reed, Gabriela. "Distance." Family Medicine 50, no. 5 (May 2, 2018): 385–86. http://dx.doi.org/10.22454/fammed.2018.905369.

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Randolph, Sarah. "Distance." Iowa Review 22, no. 3 (October 1992): 114. http://dx.doi.org/10.17077/0021-065x.4197.

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Carter, Justin. "Distance." Minnesota review 2016, no. 86 (2016): 46. http://dx.doi.org/10.1215/00265667-3457699.

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Autrey, Ken. "Distance." English Journal 94, no. 5 (May 1, 2005): 135. http://dx.doi.org/10.2307/30047383.

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Warren, Mark B. "Distance." JAMA Pediatrics 168, no. 9 (September 1, 2014): 795. http://dx.doi.org/10.1001/jamapediatrics.2014.115.

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Duckss, Weitter. "Comoving Distance- Light Travel Distance (Treatise)." Budapest International Research in Exact Sciences (BirEx) Journal 2, no. 1 (January 6, 2020): 1–12. http://dx.doi.org/10.33258/birex.v2i1.704.

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Abstract:

The discussion on the values of redshift, as well as blueshift, is based on a large increase in new evidence that in the whole volume of Universe there are gravitationally-bound objects (galaxies, clusters and super clusters of galaxies) „Using the Chandra and Hubble Space Telescopes we have now observed 72 collisions between galaxy clusters, including both ‘major’ and ‘minor’ mergers” (Harvey, 2015). That adds to a great diversity of galactic movement directions and their diverse appearances to an observer. The accent here is at the point of "clearing the early Universe" and asks questions about how these types of radiation could be measured if all galaxies were created in the early stages of Universe, which had started emitting these types of radiation. At the same time, some questions are asked about. With a redshift of 5.47, (Q0906 + 6930) light from this active galaxy is estimated to have taken around 12.3 billion light-years to reach us.. distance to this galaxy is estimated to be around 26 billion light-years (7961 Mpc). (NASA/IPAC, 2010) i.e., why the actual measured values of redshift are not applied.

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