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Journal articles on the topic 'Distance'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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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

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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18

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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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

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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21

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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22

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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23

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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24

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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25

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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26

Aleksić, Borivoje, Marija Živadinović, and Lazar Savković. "Theoretical differences between Overtaking Sight Distance and Sight Distance for Safe Overtaking." Put i saobraćaj 71, no. 1 (March 1, 2025): 25–31. https://doi.org/10.31075/pis.71.01.04.

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The current legislation mentions the terms Overtaking Sight Distance and Sight Distance for Safe Overtaking, which are contained in the Rulebook on the conditions that road facilities and other elements of public roads must meet from the aspect of road safety and the Rulebook on Traffic Signage. The paper presents the theoretical procedure for Overtaking Sight Distance from a civil engineering perspective and the theoretical procedure for Sight Distance for Safe Overtaking from a traffic engineering perspective. After that, the theoretical differences between these two Sight Distances are presented based on the applicable Rulebooks. The place and role of the considered types of Sight Distances in road engineering are stated, as well as the impact of these differences in Sight Distances on Road Safety. This paper aims to inform the wider scientific and professional public about the differences between Overtaking Sight Distance, which civil engineers apply in their field of work, and Sight Distance for Safe Overtaking, which is applied by traffic engineers in their engineering work.

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27

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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28

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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29

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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30

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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31

Berkachy, Rédina, and Laurent Donzé. "Generalisation of the Signed Distance." Mathematics 12, no. 24 (December 23, 2024): 4042. https://doi.org/10.3390/math12244042.

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This paper presents a comprehensive study of the signed distance metric for fuzzy numbers. Due to the property of directionality, this measure has been widely used. However, it has a main drawback in handling asymmetry and irregular shapes in fuzzy numbers. To overcome this rather bad feature, we introduce two new distances, the balanced signed distance (BSGD) and the generalised signed distance (GSGD), seen as generalisations of the classical signed distance. The developed distances successfully and effectively take into account the shape, the asymmetry and the overlap of fuzzy numbers. The GSGD is additionally directional, while the BSGD satisfies the requirements for being a metric of fuzzy quantities. Analytical simplifications of both distances in the case of often-used particular types of fuzzy numbers are provided to simplify the computation process, making them as simple as the classical signed distance but more realistic and precise. We empirically analyse the sensitivity of these distances. Considering several scenarios of fuzzy numbers, we also numerically compare these distances against established metrics, highlighting the advantages of the BSGD and the GSGD in capturing the shape properties of fuzzy numbers. One main finding of this research is that the defended distances capture with great precision the distance between fuzzy numbers; additionally, they are theoretically appealing and are computationally easy for traditional fuzzy numbers such as triangular, trapezoidal, Gaussian, etc., making these metrics promising.

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32

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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33

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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34

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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35

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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36

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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37

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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38

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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39

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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40

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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41

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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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

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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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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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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46

Born, Dennis-Peter, Jesús J. Ruiz-Navarro, Jenny Lorentzen, and Glenn Björklund. "Specializing When It Counts: Comparing the Dose–Time Effect of Distance Variety between Swimming and Track Running." Sports 12, no. 10 (October 9, 2024): 272. http://dx.doi.org/10.3390/sports12100272.

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Objective: To conduct a longitudinal retrospective analysis, explore the relationship between success at peak performance age and the number of different race distances athletes competed in each year (within-sport distance variety), and compare the dose–time effect of this distance variety throughout the development process between male swimmers and track runners. Methodology: Male swimmers (n = 6033) and track runners (n = 19,278) still competing at peak performance age were ranked, and the number of different race distances was extracted retrospectively for each year until early junior age (13–14-year-old category) from the databases of the European Aquatics and World Athletics federations. Firstly, correlation analysis determined the relationship between ranking at peak performance age and distance variety. Secondly, Poisson distribution provided the probability and dose–time effect of distance variety for becoming an international-class athlete at peak performance age. Results: Generally, correlation analysis revealed low coefficients (r ≤ 0.22) but significant effects (p < 0.001) for larger distance variety and success at peak performance age. Poisson distribution revealed the highest probability of becoming an international-class swimmer when competing in 2–4 race distances at junior age, depending on the primary race distance. The dose–time effect indicated a gradual reduction in the number of race distances as athletes approached peak performance age, narrowing down to 1–2, 2–3, and 3–4 distances for sprint, middle-, and long-distance races, respectively. Track runners exhibited a lower distance variety than swimmers, with a consistent optimum of 1–2 race distances across the age groups. Conclusions: The present findings including data of the most combined race distances for each primary race distance and a comparison between swimming and track running provide new background information to challenge traditional training regimes and help establish new strategies for long-term athlete development.

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Sari, Y., P. B. Prakoso, and A. R. Baskara. "Evaluation the influence of distance-based K-means method for detecting moving vehicles." IOP Conference Series: Materials Science and Engineering 1212, no. 1 (January 1, 2022): 012044. http://dx.doi.org/10.1088/1757-899x/1212/1/012044.

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Abstract Detecting moving vehicles is one of important elements in the applications of Intelligent Transport System (ITS). Detecting moving vehicles is also part of the detection of moving objects. K-Means method has been successfully applied to unsupervised cluster pixels for the detection of moving objects. In general, K-Means is a heuristic algorithm that partitioned the data set into K clusters by minimizing the number of squared distances in each cluster. In this paper, the K-Means algorithm applies Euclidean distance, Manhattan distance, Canberra distance, Chebyshev distance and Braycurtis distance. The aim of this study is to compare and evaluate the implementation of these distances in the K-Means clustering algorithm. The comparison is done with the basis of K-Means assessed with various evaluation paramaters, namely MSE, PSNR, SSIM and PCQI. The results exhibit that the Manhattan distance delivers the best MSE, PSNR, SSIM and PCQI values compared to other distances. Whereas for data processing time exposes that the Braycurtis distance has more advantages.

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Witmer, Bob G., and Paul B. Kline. "Judging Perceived and Traversed Distance in Virtual Environments." Presence: Teleoperators and Virtual Environments 7, no. 2 (April 1998): 144–67. http://dx.doi.org/10.1162/105474698565640.

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The ability to accurately estimate distance is an essential component of navigating large-scale spaces. Although the factors that influence distance estimation have been a topic of research in real-world environments for decades and are well known, research on distance estimation in virtual environments (VEs) has only just begun. Initial investigations of distance estimation in VEs suggest that observers are less accurate in estimating distance in VEs than in the real world (Lampton et al., 1995). Factors influencing distance estimates may be divided into those affecting perceived distance (visual cues only) and those affecting traversed distance to include visual, cognitive, and proprioceptive cues. To assess the contribution of the various distance cues in VEs, two experiments were conducted. The first required a static observer to estimate the distance to a cylinder placed at various points along a 130-foot hallway. This experiment examined the effects of floor texture, floor pattern, and object size on distance estimates in a VE. The second experiment required a moving observer to estimate route segment distances and total route distances along four routes, each totaling 1210 feet. This experiment assessed the effects of movement method, movement speed, compensatory cues, and wall texture density. Results indicate that observers underestimate distances both in VEs and in the real world, but the underestimates are more extreme in VEs. Texture did not reliably affect the distance estimates, providing no compensation for the gross underestimates of distance in VE. Traversing a distance improves the ability to estimate that distance, but more natural means of moving via a treadmill do not necessarily improve distance estimates over traditional methods of moving in VE (e.g., using a joystick). The addition of compensatory cues (tone every 10 feet traversed on alternate route segments) improves VE distance estimation to almost perfect performance.

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Predebon, John. "Convergence Responses to Monocularly Viewed Objects: Implications for Distance Perception." Perception 23, no. 3 (March 1994): 303–19. http://dx.doi.org/10.1068/p230303.

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In four experiments the role of dark vergence and the implied distance from the familiar-size and suggested-size cues to distance on the convergence response was investigated. A nonius-alignment technique was used to measure the convergence response in total darkness (dark vergence) and the fusion-free convergence response to monocularly viewed objects presented at a distance of 75 cm under otherwise reduced stimulus conditions. Observers also estimated the size and distance of the objects. The results indicated a significant association between individuals' dark-vergence distances and the convergence distances to the objects. Furthermore, the convergence response was influenced by the implied distance from the familiar-size cue but not by the implied distance from size suggestions. Both the familiar-size and the suggested-size cues influenced reports of distance. The implications of these findings for distance perception are discussed with particular reference to the familiar-size cue to distance.

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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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