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1

Keenan, Alexandra B., Megan L. Wojciechowicz, Zichen Wang, et al. "Connectivity Mapping: Methods and Applications." Annual Review of Biomedical Data Science 2, no. 1 (2019): 69–92. http://dx.doi.org/10.1146/annurev-biodatasci-072018-021211.

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Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been u
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2

Huang, Haiqing, and Mingzhou Ding. "Linking Functional Connectivity and Structural Connectivity Quantitatively: A Comparison of Methods." Brain Connectivity 6, no. 2 (2016): 99–108. http://dx.doi.org/10.1089/brain.2015.0382.

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3

Li, Yinwei, Guo-Ping Jiang, Meng Wu, and Yurong Song. "Methods to Measure the Network Path Connectivity." Security and Communication Networks 2020 (November 10, 2020): 1–10. http://dx.doi.org/10.1155/2020/8836590.

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The functionalities, such as connectivity and communication capability of complex networks, are related to the number and length of paths between node pairs in the networks. In this paper, we propose a new path connectivity measure by considering the number and length of paths of the network (PCNL) to evaluate network path connectivity. By comparing the PCNL with the typical natural connectivity, we prove the effectiveness of the PCNL to measure the path connectivity of networks. Because of the importance of the shortest paths, we further propose the shortest paths connectivity measure (SPCNL)
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4

Massaroppe, Lucas, and Luiz Baccalá. "Kernel Methods for Nonlinear Connectivity Detection." Entropy 21, no. 6 (2019): 610. http://dx.doi.org/10.3390/e21060610.

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In this paper, we show that the presence of nonlinear coupling between time series may be detected using kernel feature space F representations while dispensing with the need to go back to solve the pre-image problem to gauge model adequacy. This is done by showing that the kernelized auto/cross sequences in F can be computed from the model rather than from prediction residuals in the original data space X . Furthermore, this allows for reducing the connectivity inference problem to that of fitting a consistent linear model in F that works even in the case of nonlinear interactions in the X -s
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5

Nagarajan, S. S., J. P. Owen, L. B. Hinkley, H. T. Attias, and K. Sekihara. "Robust methods for estimating functional connectivity." NeuroImage 47 (July 2009): S169. http://dx.doi.org/10.1016/s1053-8119(09)71816-6.

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6

Kim, Sung-Ho, and Chang-Hyun Park. "Statistical methods for modelling functional neuro-connectivity." Korean Journal of Applied Statistics 29, no. 6 (2016): 1129–45. http://dx.doi.org/10.5351/kjas.2016.29.6.1129.

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7

Márquez-Chamorro, Alfonso E., and Jesús S. Aguilar-Ruiz. "Soft Computing Methods for Disulfide Connectivity Prediction." Evolutionary Bioinformatics 11 (January 2015): EBO.S25349. http://dx.doi.org/10.4137/ebo.s25349.

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8

Karas, Marta, Damian Brzyski, Mario Dzemidzic, et al. "Brain Connectivity-Informed Regularization Methods for Regression." Statistics in Biosciences 11, no. 1 (2017): 47–90. http://dx.doi.org/10.1007/s12561-017-9208-x.

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9

Ma, Wen Peng, Zhong Hua Lu, and Xiao Dong Hu. "Parallel Solutions for Node Connectivity Based Mesh Moving Methods on Different Parallel Architectures." Applied Mechanics and Materials 220-223 (November 2012): 2330–37. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2330.

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Methods for updating the computational mesh have been studied for many years and there are mainly two kinds of mesh moving algorithms known as node connectivity based and no node connectivity based respectively. As the node connectivity based mesh moving methods are universal for both structured and unstructured mesh but of high computational cost at the same time, detailed attentions are paid to in this paper to balance its advantages and disadvantages. The format and the features of the derived linear equations in node connectivity algorithms are analyzed firstly and the choice of solver for
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10

Liang, Xiuguang, Qiang Dong, and Yunyang Liu. "The Methods for Connectivity Judgment on Reservoir Layers." IOP Conference Series: Earth and Environmental Science 100 (December 2017): 012008. http://dx.doi.org/10.1088/1755-1315/100/1/012008.

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11

Melie Garcı´a, L., Y. Iturria Medina, E. Canales Rodrı´guez, Y. Alemán Gómez, and P. A. Valdés Hernández. "24. Methods to determine anatomical and functional connectivity." Clinical Neurophysiology 119, no. 9 (2008): e105. http://dx.doi.org/10.1016/j.clinph.2008.04.040.

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12

Phillips, Payton, Melissa M. Clark, Suraj Baral, Erin L. Koen, and Jeff Bowman. "Comparison of methods for estimating omnidirectional landscape connectivity." Landscape Ecology 36, no. 6 (2021): 1647–61. http://dx.doi.org/10.1007/s10980-021-01254-2.

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13

Lee, Sophie A., Christopher I. Jarvis, W. John Edmunds, Theodoros Economou, and Rachel Lowe. "Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions." Journal of The Royal Society Interface 18, no. 178 (2021): 20210096. http://dx.doi.org/10.1098/rsif.2021.0096.

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Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local
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14

Liu, Jinzi. "Potential for Evaluation of Interwell Connectivity under the Effect of Intraformational Bed in Reservoirs Utilizing Machine Learning Methods." Geofluids 2020 (June 10, 2020): 1–10. http://dx.doi.org/10.1155/2020/1651549.

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Machine learning method has gradually become an important and effective method to analyze reservoir parameters in reservoir numerical simulation. This paper provides a machine learning method to evaluate the connectivity between injection and production wells controlled by interlayer in reservoir. In this paper, Back Propagation (BP) and Convolutional Neural Networks (CNNs) are used to train the dynamic data with the influence of interlayer control connectivity in the reservoir layer as the training model. The dataset is trained with dynamic production data under different permeability, interl
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15

Heo, Nojeong. "Mixed Deployment Methods for Reinforcing Connectivity of Sensor Networks." Journal of the Institute of Electronics and Information Engineers 51, no. 6 (2014): 169–74. http://dx.doi.org/10.5573/ieie.2014.51.6.169.

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16

Blume, Theresa, and H. J. Ilja van Meerveld. "From hillslope to stream: methods to investigate subsurface connectivity." Wiley Interdisciplinary Reviews: Water 2, no. 3 (2015): 177–98. http://dx.doi.org/10.1002/wat2.1071.

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17

Chen, Kai, Azeezat Azeez, Donna Y. Chen, and Bharat B. Biswal. "Resting-State Functional Connectivity: Signal Origins and Analytic Methods." Neuroimaging Clinics of North America 30, no. 1 (2020): 15–23. http://dx.doi.org/10.1016/j.nic.2019.09.012.

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18

Anastasiades, Paul G., Andre Marques-Smith, and Simon J. B. Butt. "Studies of cortical connectivity using optical circuit mapping methods." Journal of Physiology 596, no. 2 (2017): 145–62. http://dx.doi.org/10.1113/jp273463.

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19

Brix, Kolja, Ralf Massjung, and Alexander Voss. "Refinement and Connectivity Algorithms for Adaptive Discontinuous Galerkin Methods." SIAM Journal on Scientific Computing 33, no. 1 (2011): 66–101. http://dx.doi.org/10.1137/090767418.

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20

Choe, Ann S., Mary Beth Nebel, Anita D. Barber, et al. "Comparing test-retest reliability of dynamic functional connectivity methods." NeuroImage 158 (September 2017): 155–75. http://dx.doi.org/10.1016/j.neuroimage.2017.07.005.

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21

Thompson, William Hedley, Craig Geoffrey Richter, Pontus Plavén-Sigray, and Peter Fransson. "Simulations to benchmark time-varying connectivity methods for fMRI." PLOS Computational Biology 14, no. 5 (2018): e1006196. http://dx.doi.org/10.1371/journal.pcbi.1006196.

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22

Sanchez-Romero, Ruben, and Michael W. Cole. "Combining Multiple Functional Connectivity Methods to Improve Causal Inferences." Journal of Cognitive Neuroscience 33, no. 2 (2021): 180–94. http://dx.doi.org/10.1162/jocn_a_01580.

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Cognition and behavior emerge from brain network interactions, suggesting that causal interactions should be central to the study of brain function. Yet, approaches that characterize relationships among neural time series—functional connectivity (FC) methods—are dominated by methods that assess bivariate statistical associations rather than causal interactions. Such bivariate approaches result in substantial false positives because they do not account for confounders (common causes) among neural populations. A major reason for the dominance of methods such as bivariate Pearson correlation (wit
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23

Mountantonakis, Michalis, and Yannis Tzitzikas. "High Performance Methods for Linked Open Data Connectivity Analytics." Information 9, no. 6 (2018): 134. http://dx.doi.org/10.3390/info9060134.

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24

Godley, Brendan J., Castro Barbosa, Michael Bruford, et al. "Unravelling migratory connectivity in marine turtles using multiple methods." Journal of Applied Ecology 47, no. 4 (2010): 769–78. http://dx.doi.org/10.1111/j.1365-2664.2010.01817.x.

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25

Lin, Kequan, Lu Li, Yifei Dai, et al. "A comprehensive evaluation of connectivity methods for L1000 data." Briefings in Bioinformatics 21, no. 6 (2019): 2194–205. http://dx.doi.org/10.1093/bib/bbz129.

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Abstract The methodologies for evaluating similarities between gene expression profiles of different perturbagens are the key to understanding mechanisms of actions (MoAs) of unknown compounds and finding new indications for existing drugs. L1000-based next-generation Connectivity Map (CMap) data is more than a thousand-fold scale-up of the CMap pilot dataset. Although several systematic evaluations have been performed individually to assess the accuracy of the methodologies for the CMap pilot study, the performance of these methodologies needs to be re-evaluated for the L1000 data. Here, usin
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26

Vik, B., K. E. Sylta, and A. Skauge. "Connectivity in Vuggy Carbonates, New Experimental Methods and Applications." Transport in Porous Media 93, no. 3 (2012): 561–75. http://dx.doi.org/10.1007/s11242-012-9969-0.

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27

Pakman, Ari, Jonathan Huggins, Carl Smith, and Liam Paninski. "Fast state-space methods for inferring dendritic synaptic connectivity." Journal of Computational Neuroscience 36, no. 3 (2013): 415–43. http://dx.doi.org/10.1007/s10827-013-0478-0.

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28

Shahhosseini, Yasaman, and Michelle F. Miranda. "Functional Connectivity Methods and Their Applications in fMRI Data." Entropy 24, no. 3 (2022): 390. http://dx.doi.org/10.3390/e24030390.

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The availability of powerful non-invasive neuroimaging techniques has given rise to various studies that aim to map the human brain. These studies focus on not only finding brain activation signatures but also on understanding the overall organization of functional communication in the brain network. Based on the principle that distinct brain regions are functionally connected and continuously share information with each other, various approaches to finding these functional networks have been proposed in the literature. In this paper, we present an overview of the most common methods to estima
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29

Chauhan, Nishant, and Byung-Jae Choi. "Comparison of Functional Connectivity Analysis Methods in Alzheimer’s Disease." Applied Sciences 12, no. 16 (2022): 8096. http://dx.doi.org/10.3390/app12168096.

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This paper intends to present a comparative review of functional connectivity (FC) analysis methods and their computational methodologies measured through functional magnetic resonance imaging (fMRI). The fMRI technique has been established as a powerful tool for identifying and visualizing the active brain areas in response to certain stimuli and tasks. FC is a metric for the interaction between various brain regions. The synchronization of the functional activity between non-adjacent brain regions is reflected in FC, and changes in FC occur earlier than changes in the physical brain structur
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30

Batenkov, Aleksandr, Kirill Batenkov, and Aleksandr Fokin. "Methods for Formation of Telecommunication Network States Sets for Different Measures of Connectivity." SPIIRAS Proceedings 19, no. 3 (2020): 644–73. http://dx.doi.org/10.15622/sp.2020.19.3.7.

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Reliability, survivability, and stability analysis tasks are typical not only for telecommunications, but also for systems whose components are subject to one or more types of failures, such as transport, power, mechanical systems, integrated circuits, and even software. The logical approach involves the decomposition of the system into a number of small functional elements, and within telecommunications networks they are usually separate network devices (switches, routers, terminals, etc.), as well as communication lines between them (copper-core, fiber-optic, coaxial cables, wireless media,
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31

Calò, Antonio, Fabiana C. Félix-Hackradt, Jessica Garcia, et al. "A review of methods to assess connectivity and dispersal between fish populations in the Mediterranean Sea." Advances in Oceanography and Limnology 4, no. 2 (2013): 150. http://dx.doi.org/10.4081/aiol.2013.5342.

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Fish populations are linked to each other via dispersal of individuals as eggs, larvae, juveniles or adults. The understanding of this process, known as connectivity, has a pivotal role for the management of overexploited fish stocks and the development of accurate conservation strategies. Knowledge on connectivity and fish movements is considered fundamental toward the correct design of marine protected area (MPA) networks for the achievement of the benefits of protection. Connectivity patterns are still largely unknown worldwide. A general lack of knowledge is particularly evident for the Me
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32

Lowe, Mark J., Ken E. Sakaie, Erik B. Beall, et al. "Modern Methods for Interrogating the Human Connectome." Journal of the International Neuropsychological Society 22, no. 2 (2016): 105–19. http://dx.doi.org/10.1017/s1355617716000060.

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AbstractObjectives: Connectionist theories of brain function took hold with the seminal contributions of Norman Geschwind a half century ago. Modern neuroimaging techniques have expanded the scientific interest in the study of brain connectivity to include the intact as well as disordered brain. Methods: In this review, we describe the most common techniques used to measure functional and structural connectivity, including resting state functional MRI, diffusion MRI, and electroencephalography and magnetoencephalography coherence. We also review the most common analytical approaches used for e
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33

Baker, Cordell M., Joshua D. Burks, Robert G. Briggs, et al. "A Connectomic Atlas of the Human Cerebrum—Chapter 1: Introduction, Methods, and Significance." Operative Neurosurgery 15, suppl_1 (2018): S1—S9. http://dx.doi.org/10.1093/ons/opy253.

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ABSTRACT BACKGROUND As knowledge of the brain has increased, clinicians have learned that the cerebrum is composed of complex networks that interact to execute key functions. While neurosurgeons can typically predict and preserve primary cortical function through the primary visual and motor cortices, preservation of higher cognitive functions that are less well localized in regions previously deemed “silent” has proven more difficult. This suggests these silent cortical regions are more anatomically complex and redundant than our previous methods of inquiry can explain, and that progress in c
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34

Schwarz, Stefan, Blanca Ramos Elbal, Erich Zöchmann, Ljiljana Marijanovic, and Stefan Pratschner. "Dependable wireless connectivity: insights and methods for 5G and beyond." e & i Elektrotechnik und Informationstechnik 135, no. 7 (2018): 449–55. http://dx.doi.org/10.1007/s00502-018-0646-z.

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35

Celic, Luka, and Ratko Magjarevic. "Seamless connectivity architecture and methods for IoT and wearable devices." Automatika 61, no. 1 (2019): 21–34. http://dx.doi.org/10.1080/00051144.2019.1660036.

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36

Sarmukadam, Kimaya, Vicki Bitsika, Christopher F. Sharpley, Mary M. E. McMillan, and Linda L. Agnew. "Comparing different EEG connectivity methods in young males with ASD." Behavioural Brain Research 383 (April 2020): 112482. http://dx.doi.org/10.1016/j.bbr.2020.112482.

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37

Li, Kaiming, Lei Guo, Jingxin Nie, Gang Li, and Tianming Liu. "Review of methods for functional brain connectivity detection using fMRI." Computerized Medical Imaging and Graphics 33, no. 2 (2009): 131–39. http://dx.doi.org/10.1016/j.compmedimag.2008.10.011.

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38

Recker, RR, RT Smith, and DB Kimmel. "Loss of trabecular connectivity in osteoporosis demonstrated with independent methods." Bone 13, no. 5 (1992): A28. http://dx.doi.org/10.1016/8756-3282(92)90567-g.

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39

Rossini, P. M., R. Di Iorio, M. Bentivoglio, et al. "Methods for analysis of brain connectivity: An IFCN-sponsored review." Clinical Neurophysiology 130, no. 10 (2019): 1833–58. http://dx.doi.org/10.1016/j.clinph.2019.06.006.

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40

Bissaker, Edward, Bishnu Lamichhane, and David Jenkins. "Connectivity aware simulated annealing kernel methods for coke microstructure generation." ANZIAM Journal 63 (July 28, 2022): C123—C137. http://dx.doi.org/10.21914/anziamj.v63.17187.

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A vital input for steel manufacture is a coal-derived solid fuel called coke. Digital reconstructions and simulations of coke are valuable tools to analyse and test coke properties. We implement biased voxel iteration into a simulated annealing method via a kernel convolution to reduce the number of iterations required to generate a digital coke microstructure. We demonstrate that voxel connectivity assumptions impact the number of iterations and reduce the normalised computation time required to generate a digital microstructure by as much as 70%. References L. De Floriani, U. Fugacci, and F.
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Тихомиров, Виктор, Viktor Tikhomirov, Александр Стриженок, and Aleksandr Strizhenok. "METHODS OF MATERIAL CHOICE FOR COG-WHEELS." Bulletin of Bryansk state technical university 2016, no. 2 (2016): 99–103. http://dx.doi.org/10.12737/20251.

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The method for material choice of lowcarbon alloyed steel for manufacturing cog-wheels of axial reduction gear of locomotives of 2TE25A, TEM 18 is considered. The method allows choosing the best material for cog-wheels taking into account basic physical stressstrain and tribological characteristics and operation conditions. The material choice is carried out on the basis of three criteria: value, strength, and wear-resistance for three kinds of steel. The procedure of material choice provides for the definition of the priori-ty on every criterion. As an illustration there is shown an example.
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42

Rubanov, I. V., and M. Y. Kovalyov. "Computational methods for airspace sectorisation." Informatics 17, no. 4 (2021): 7–21. http://dx.doi.org/10.37661/1816-0301-2020-17-4-7-21.

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A problem of combining elementary sectors of an airspace region is considered, in which a minimum number of combined sectors must be obtained with restrictions on their load and feasibility of combinations such as the requirement of the space connectivity or the membership of a given set of permissible combinations. Computational methods are proposed and tested to be used for solution of general problems of airspace sectorization. In particular, two types of combinatorial algorithms are proposed for constructing partitions of a finite set with specified element weights and graph-theoretical re
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43

Rubanov, I. V., and M. Y. Kovalyov. "Computational methods for airspace sectorisation." Informatics 17, no. 4 (2021): 7–21. http://dx.doi.org/10.37661/1816-0301-2020-17-4-7-21.

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A problem of combining elementary sectors of an airspace region is considered, in which a minimum number of combined sectors must be obtained with restrictions on their load and feasibility of combinations such as the requirement of the space connectivity or the membership of a given set of permissible combinations. Computational methods are proposed and tested to be used for solution of general problems of airspace sectorization. In particular, two types of combinatorial algorithms are proposed for constructing partitions of a finite set with specified element weights and graph-theoretical re
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44

Sanqing Hu, Guojun Dai, G. A. Worrell, Qionghai Dai, and Hualou Liang. "Causality Analysis of Neural Connectivity: Critical Examination of Existing Methods and Advances of New Methods." IEEE Transactions on Neural Networks 22, no. 6 (2011): 829–44. http://dx.doi.org/10.1109/tnn.2011.2123917.

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45

Nikologianni, Anastasia, Alessandro Betta, and Alessandro Gretter. "Contribution of Conceptual-Drawing Methods to Raise Awareness on Landscape Connectivity: Socio-Environmental Analysis in the Regional Context of Trentino (Italy)." Sustainability 14, no. 13 (2022): 7975. http://dx.doi.org/10.3390/su14137975.

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This paper deals with landscape understanding and connectivity from an ecological as well as a human perspective. It is based on a broader research study known as EIT Climate-KIC SATURN (System and sustainable Approach to virTuous interaction of Urban and Rural LaNdscapes) co-funded by the European Institute of Innovation & Technology (EIT), Climate-KIC and the institutions participating in this project. The aim of the study was to explore how landscape connectivity is perceived, on urban and regional scales, by decision-makers and key stakeholders as well as the impact this can have on bu
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46

Witte, Herbert, Laura Astolfi, Lutz Leistritz, and Fabio Babiloni. "Advanced methods for the estimation of human brain activity and connectivity." Journal of Physiology-Paris 103, no. 6 (2009): 305. http://dx.doi.org/10.1016/j.jphysparis.2009.07.001.

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47

Lee, Donghyung, Ting-Hao Huang, Aubrie De La Cruz, Antuca Callejas, and Carlos Lois. "Methods to investigate the structure and connectivity of the nervous system." Fly 11, no. 3 (2017): 224–38. http://dx.doi.org/10.1080/19336934.2017.1295189.

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48

Poor, Erin E., Colby Loucks, Andrew Jakes, and Dean L. Urban. "Comparing Habitat Suitability and Connectivity Modeling Methods for Conserving Pronghorn Migrations." PLoS ONE 7, no. 11 (2012): e49390. http://dx.doi.org/10.1371/journal.pone.0049390.

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49

Li, Yichen, Rebecca Saxe, and Stefano Anzellotti. "Intersubject MVPD: Empirical comparison of fMRI denoising methods for connectivity analysis." PLOS ONE 14, no. 9 (2019): e0222914. http://dx.doi.org/10.1371/journal.pone.0222914.

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50

Du, Shuyi, Ruifei Wang, Chenji Wei, et al. "The Connectivity Evaluation Among Wells in Reservoir Utilizing Machine Learning Methods." IEEE Access 8 (2020): 47209–19. http://dx.doi.org/10.1109/access.2020.2976910.

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