Готові списки джерел за темами / Rank analysis

Добірка наукової літератури з теми "Rank analysis"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Rank analysis"

1

Grasedyck, Lars, and Wolfgang Hackbusch. "An Introduction to Hierarchical (H-) Rank and TT-Rank of Tensors with Examples." Computational Methods in Applied Mathematics 11, no. 3 (2011): 291–304. http://dx.doi.org/10.2478/cmam-2011-0016.

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Анотація:
Abstract We review two similar concepts of hierarchical rank of tensors (which extend the matrix rank to higher order tensors): the TT-rank and the H-rank (hierarchical or H-Tucker rank). Based on this notion of rank, one can define a data-sparse representation of tensors involving O(dnk + dk^3) data for order d tensors with mode sizes n and rank k. Simple examples underline the differences and similarities between the different formats and ranks. Finally, we derive rank bounds for tensors in one of the formats based on the ranks in the other format.
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2

Storozhuk, K. V. "Asymptotic Rank Theorems." Algebra and Logic 58, no. 4 (September 2019): 337–44. http://dx.doi.org/10.1007/s10469-019-09555-x.

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3

Chandrasekaran, Shivkumar, and Ilse C. F. Ipsen. "On Rank-Revealing Factorisations." SIAM Journal on Matrix Analysis and Applications 15, no. 2 (April 1994): 592–622. http://dx.doi.org/10.1137/s0895479891223781.

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4

Bahmani, Sohail, and Kiryung Lee. "Low-Rank Matrix Estimation from Rank-One Projections by Unlifted Convex Optimization." SIAM Journal on Matrix Analysis and Applications 42, no. 3 (January 2021): 1119–47. http://dx.doi.org/10.1137/20m1330099.

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5

de Silva, Vin, and Lek-Heng Lim. "Tensor Rank and the Ill-Posedness of the Best Low-Rank Approximation Problem." SIAM Journal on Matrix Analysis and Applications 30, no. 3 (January 2008): 1084–127. http://dx.doi.org/10.1137/06066518x.

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6

Kwiatkowski, J., and Y. Lacroix. "Multiplicity, rank pairs." Journal d'Analyse Mathématique 71, no. 1 (December 1997): 205–35. http://dx.doi.org/10.1007/bf02788031.

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7

King, Jonathan L. "Joining-rank and the structure of finite rank mixing transformations." Journal d'Analyse Mathématique 51, no. 1 (December 1988): 182–227. http://dx.doi.org/10.1007/bf02791123.

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8

de Juan, Anna, Susana Navea, Josef Diewok, and Romà Tauler. "Local rank exploratory analysis of evolving rank-deficient systems." Chemometrics and Intelligent Laboratory Systems 70, no. 1 (January 2004): 11–21. http://dx.doi.org/10.1016/j.chemolab.2003.09.002.

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9

Tyrtyshnikov, Eugene E. "Tensor decompositions and rank increment conjecture." Russian Journal of Numerical Analysis and Mathematical Modelling 35, no. 4 (August 26, 2020): 239–46. http://dx.doi.org/10.1515/rnam-2020-0020.

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Анотація:
AbstractSome properties of tensor ranks and the non-closeness issue of sets with given restrictions on the rank of tensors entering those sets are studied. It is proved that the rank of the d-dimensional Laplacian equals d. The following conjecture is formulated: for any tensor of non-maximal rank there exists a nonzero decomposable tensor (tensor of rank 1) such that the rank increases by one after adding this tensor. In the general case, it is proved that this property holds algebraically almost everywhere for complex tensors of fixed size whose rank is strictly less than the generic rank.
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10

Hadwin, Don, and David R. Larson. "Completely rank-nonincreasing linear maps." Journal of Functional Analysis 199, no. 1 (April 2003): 210–27. http://dx.doi.org/10.1016/s0022-1236(02)00091-5.

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Дисертації з теми "Rank analysis"

1

Busse, Ludwig M. Orbanz Peter Buhmann Joachim M. Buhmann Joachim M. Buhmann Joachim M. "Cluster analysis of heterogeneous rank data." Zurich : ETH Department of Computer Science, Institute of Computational Sciences, 2007. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=350.

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2

Cerny, C. M. "On higher rank Cuntz-Pimsner algebras." Thesis, University of Nottingham, 2012. http://eprints.nottingham.ac.uk/13243/.

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3

Chan, Yip-cheung, and 陳葉祥. "Minimal rank of abelian group matrices." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B29797512.

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4

Chan, Yip-cheung. "Minimal rank of abelian group matrices /." Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B1759215X.

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5

Hatefi, Armin. "Mixture model analysis with rank-based samples." Statistica Sinica, 2013. http://hdl.handle.net/1993/23849.

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Анотація:
Simple random sampling (SRS) is the most commonly used sampling design in data collection. In many applications (e.g., in fisheries and medical research) quantification of the variable of interest is either time-consuming or expensive but ranking a number of sampling units, without actual measurement on them, can be done relatively easy and at low cost. In these situations, one may use rank-based sampling (RBS) designs to obtain more representative samples from the underlying population and improve the efficiency of the statistical inference. In this thesis, we study the theory and application of the finite mixture models (FMMs) under RBS designs. In Chapter 2, we study the problems of Maximum Likelihood (ML) estimation and classification in a general class of FMMs under different ranked set sampling (RSS) designs. In Chapter 3, deriving Fisher information (FI) content of different RSS data structures including complete and incomplete RSS data, we show that the FI contained in each variation of the RSS data about different features of FMMs is larger than the FI contained in their SRS counterparts. There are situations where it is difficult to rank all the sampling units in a set with high confidence. Forcing rankers to assign unique ranks to the units (as RSS) can lead to substantial ranking error and consequently to poor statistical inference. We hence focus on the partially rank-ordered set (PROS) sampling design, which is aimed at reducing the ranking error and the burden on rankers by allowing them to declare ties (partially ordered subsets) among the sampling units. Studying the information and uncertainty structures of the PROS data in a general class of distributions, in Chapter 4, we show the superiority of the PROS design in data analysis over RSS and SRS schemes. In Chapter 5, we also investigate the ML estimation and classification problems of FMMs under the PROS design. Finally, we apply our results to estimate the age structure of a short-lived fish species based on the length frequency data, using SRS, RSS and PROS designs.
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6

Subramanian, Nandita. "Analysis of Rank Distance for Malware Classification." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479823187035784.

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7

Ziegler, Caleb. "On Factors of Rank One Subshifts." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1157623/.

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Анотація:
Rank one subshifts are dynamical systems generated by a regular combinatorial process based on sequences of positive integers called the cut and spacer parameters. Despite the simple process that generates them, rank one subshifts comprise a generic set and are the source of many counterexamples. As a result, measure theoretic rank one subshifts, called rank one transformations, have been extensively studied and investigations into rank one subshifts been the basis of much recent work. We will answer several open problems about rank one subshifts. We completely classify the maximal equicontinuous factor for rank one subshifts, so that this factor can be computed from the parameters. We use these methods to classify when large classes of rank one subshifts have mixing properties. Also, we completely classify the situation when a rank one subshift can be a factor of another rank one subshift.
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8

Kang, Zhao. "LOW RANK AND SPARSE MODELING FOR DATA ANALYSIS." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/dissertations/1366.

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Анотація:
Nowadays, many real-world problems must deal with collections of high-dimensional data. High dimensional data usually have intrinsic low-dimensional representations, which are suited for subsequent analysis or processing. Therefore, finding low-dimensional representations is an essential step in many machine learning and data mining tasks. Low-rank and sparse modeling are emerging mathematical tools dealing with uncertainties of real-world data. Leveraging on the underlying structure of data, low-rank and sparse modeling approaches have achieved impressive performance in many data analysis tasks. Since the general rank minimization problem is computationally NP-hard, the convex relaxation of original problem is often solved. One popular heuristic method is to use the nuclear norm to approximate the rank of a matrix. Despite the success of nuclear norm minimization in capturing the low intrinsic-dimensionality of data, the nuclear norm minimizes not only the rank, but also the variance of matrix and may not be a good approximation to the rank function in practical problems. To mitigate above issue, this thesis proposes several nonconvex functions to approximate the rank function. However, It is often difficult to solve nonconvex problem. In this thesis, an optimization framework for nonconvex problem is further developed. The effectiveness of this approach is examined on several important applications, including matrix completion, robust principle component analysis, clustering, and recommender systems. Another issue associated with current clustering methods is that they work in two separate steps including similarity matrix computation and subsequent spectral clustering. The learned similarity matrix may not be optimal for subsequent clustering. Therefore, a unified algorithm framework is developed in this thesis. To capture the nonlinear relations among data points, we formulate this method in kernel space. Furthermore, the obtained continuous spectral solutions could severely deviate from the true discrete cluster labels, a discrete transformation is further incorporated in our model. Finally, our framework can simultaneously learn similarity matrix, kernel, and discrete cluster labels. The performance of the proposed algorithms is established through extensive experiments. This framework can be easily extended to semi-supervised classification.
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9

Zhang, Kai. "Kernel-based clustering and low rank approximation /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20ZHANG.

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10

Kudela, László [Verfasser], Ernst [Akademischer Betreuer] Rank, Zohar [Gutachter] Yosibash, Ernst [Gutachter] Rank, and Uwe [Gutachter] Stilla. "Image-based finite element analysis / László Kudela ; Gutachter: Zohar Yosibash, Ernst Rank, Uwe Stilla ; Betreuer: Ernst Rank." München : Universitätsbibliothek der TU München, 2020. http://d-nb.info/1222161648/34.

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Книги з теми "Rank analysis"

1

Reinsel, Gregory C. Multivariate reduced-rank regression: Theory and applications. New York: Springer, 1998.

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2

Maurice, Kendall. Rank correlation methods. 5th ed. London: Edward Arnold, 1990.

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3

Leeden, Rien van der. Reduced rank regression with structured residuals. Leiden: DSWO Press, 1990.

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4

Gutenbrunner, C. Tests of linear hypotheses based on regression rank scores. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1992.

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5

Fu, Yun, ed. Low-Rank and Sparse Modeling for Visual Analysis. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12000-3.

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6

Naelten, Mark van. An alternative approach to rank-size analysis in geography. [Nijmegen]: Catholic University of Nijmegen (The Netherlands), Faculty of Geography and Prehistory, 1988.

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7

Reduced rank regression: With applications to quantitative structure-activity relationships. Heidelberg: Physica-Verlag, 1995.

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8

Peters, Dawn. A multivariate signed-rank test for the one-sample location problem. Corvallis, Or: Dept. of Statistics, Oregon State University, 1988.

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9

Peters, Dawn. A multivariate signed-rank test for the one-sample location problem. Corvallis, Or: Dept. of Statistics, Oregon State University, 1988.

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10

Isaksson, Sven. Food and rank in early medieval time. Stockholm: Archaeological Research Laboratory, Stockholm University, 2000.

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Частини книг з теми "Rank analysis"

1

Kozen, Dexter C. "Matrix Rank." In The Design and Analysis of Algorithms, 176–80. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-4400-4_33.

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2

Gatignon, Hubert. "Rank-Ordered Data." In Statistical Analysis of Management Data, 231–51. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-1270-1_9.

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3

Hackbusch, Wolfgang. "Rank-r Matrices." In Hierarchical Matrices: Algorithms and Analysis, 25–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47324-5_2.

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4

Gatignon, Hubert. "Rank-Ordered Data." In Statistical Analysis of Management Data, 269–96. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-8594-0_9.

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5

Folsom, Amanda. "Modularity and the distinct rank function." In Combinatory Analysis, 181–91. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7858-4_11.

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6

Cleaver, Graham. "Ranking and Rank-Rating." In Descriptive Analysis in Sensory Evaluation, 447–91. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781118991657.ch12.

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7

Liu, Yipeng, Jiani Liu, Zhen Long, and Ce Zhu. "Low-Rank Tensor Recovery." In Tensor Computation for Data Analysis, 93–114. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74386-4_4.

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8

Reinsel, Gregory C., and Raja P. Velu. "Alternate Procedures for Analysis of Multivariate Regression Models." In Multivariate Reduced-Rank Regression, 225–31. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2853-8_9.

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9

Hofer, Eduard. "Step 5: Rank Uncertainties." In The Uncertainty Analysis of Model Results, 209–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76297-5_6.

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10

David, Liron, and Avishai Wool. "Fast Analytical Rank Estimation." In Constructive Side-Channel Analysis and Secure Design, 168–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16350-1_10.

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Тези доповідей конференцій з теми "Rank analysis"

1

Saxena, Akrati, Ralucca Gera, and S. R. S. Iyengar. "Observe Locally Rank Globally." In ASONAM '17: Advances in Social Networks Analysis and Mining 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3110025.3110063.

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2

Ding, Yuxin, Ling Xie, Zhensheng Kang, and Zhiyang Song. "Rank learning algorithm for user reputation." In 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). IEEE, 2017. http://dx.doi.org/10.1109/spac.2017.8304369.

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3

Cohen, Jeremy E., and Nicolas Gillis. "Nonnegative Low-rank Sparse Component Analysis." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682188.

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4

Bell, Kristine, Joel Johnson, Christopher Baker, Graeme Smith, and Muralidhar Rangaswamy. "Bistatic coherent MIMO clutter rank analysis." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362437.

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5

Busse, Ludwig M., Peter Orbanz, and Joachim M. Buhmann. "Cluster analysis of heterogeneous rank data." In the 24th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1273496.1273511.

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6

Huerta, Juan M. "Relative rank statistics for dialog analysis." In the Conference. Morristown, NJ, USA: Association for Computational Linguistics, 2008. http://dx.doi.org/10.3115/1613715.1613840.

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7

Zhang, Xin, and Fuchun Sun. "Circle Text Expansion as Low-Rank Textures." In 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2011. http://dx.doi.org/10.1109/icdar.2011.49.

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8

Çağlar, Mine, Abdullah Harun Karakuş, and Ioannis Karatzas. "Planar Brownian flows with rank-based characteristics." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017). Author(s), 2018. http://dx.doi.org/10.1063/1.5043936.

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9

Gillard, Jonathan, and Anatoly Zhigljavsky. "Global optimization for structured low rank approximation." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2015 (ICNAAM 2015). Author(s), 2016. http://dx.doi.org/10.1063/1.4952191.

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10

Landauskas, Mantas, Jurate Ragulskiene, and Minvydas Ragulskis. "H-rank as a control tool for discrete dynamical systems." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756604.

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Звіти організацій з теми "Rank analysis"

1

Linke, Ethan, Nazmina Mahmoudzadeh, and Darren Holland. Prioritising Foodborne Disease with Multi-Criteria Decision Analysis. Food Standards Agency, November 2021. http://dx.doi.org/10.46756/sci.fsa.gex408.

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Анотація:
In recent years, the FSA has published a series of research projects which have produced estimates of the frequency and burden of thirteen different foodborne diseases. This document outlines the methodology and results of a multi-criteria decision analysis (MCDA) used to rank them in order of their detrimental effect on UK society.
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2

Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk, and Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4470.

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Анотація:
Cluster analysis of the efficiency of the recreational forest use of the region by separate components of the recreational forest use potential is provided in the article. The main stages of the cluster analysis of the recreational forest use level based on the predetermined components were determined. Among the agglomerative methods of cluster analysis, intended for grouping and combining the objects of study, it is common to distinguish the three most common types: the hierarchical method or the method of tree clustering; the K-means Clustering Method and the two-step aggregation method. For the correct selection of clusters, a comparative analysis of several methods was performed: arithmetic mean ranks, hierarchical methods followed by dendrogram construction, K- means method, which refers to reference methods, in which the number of groups is specified by the user. The cluster analysis of forestries by twenty analytical grounds was not proved by analysis of variance, so the re-clustering of certain objects was carried out according to the nine most significant analytical features. As a result, the forestry was clustered into four clusters. The conducted cluster analysis with the use of different methods allows us to state that their combination helps to select reasonable groupings, clearly illustrate the clustering procedure and rank the obtained forestry clusters.
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3

Cody, G. D., R. E. Botto, H. Ade, and S. Wirick. The application of soft X-ray microscopy to the in-situ analysis of sporopollenin/sporinite in a rank variable suite of organic rich sediments. Office of Scientific and Technical Information (OSTI), July 1997. http://dx.doi.org/10.2172/515505.

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4

Hossain, Niamat Ullah Ibne, Farjana Nur, Raed Jaradat, Seyedmohsen Hosseini, Mohammad Marufuzzaman, Stephen Puryear, and Randy Buchanan. Metrics for assessing overall performance of inland waterway ports : a Bayesian Network based approach. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40545.

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Because ports are considered to be the heart of the maritime transportation system, thereby assessing port performance is necessary for a nation’s development and economic success. This study proposes a novel metric, namely, “port performance index (PPI)”, to determine the overall performance and utilization of inland waterway ports based on six criteria, port facility, port availability, port economics, port service, port connectivity, and port environment. Unlike existing literature, which mainly ranks ports based on quantitative factors, this study utilizes a Bayesian Network (BN) model that focuses on both quantitative and qualitative factors to rank a port. The assessment of inland waterway port performance is further analyzed based on different advanced techniques such as sensitivity analysis and belief propagation. Insights drawn from the study show that all the six criteria are necessary to predict PPI. The study also showed that port service has the highest impact while port economics has the lowest impact among the six criteria on PPI for inland waterway ports.
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5

Wang, Lili, Xuesong Wang, Yin Wu, Lingxiao Ye, Yahua Zheng, and Rui Fan. The Effects of Non-Pharmacological Therapies for Psychological State of Medical Staff in the Post-epidemic Era: A Protocol Network Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2022. http://dx.doi.org/10.37766/inplasy2022.2.0080.

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Анотація:
Review question / Objective: To compare and rank the clinical effects of Non-Pharmacological Therapies for Psychological State of Medical Staff in the Post-epidemic Eradifferent. Eligibility criteria: The published randomized controlled trials (RCT) of non-Pharmacological Therapies for Psychological State of Medical Staff in the Post-epidemic Era, regardless of age and gender. Patients had clear diagnostic criteria to be diagnosed. Interventions in the treatment group included were various types of non-pharmacological therapies, including various types of acupuncture therapies (such as simple acupuncture, electroacupuncture, warm acupuncture, acupuncture catgut embedding, Auricular therapy, or the combination of acupuncture and other Non-Pharmacological Therapies), meditation, Baduanjin, Tai Chi, aerobic exercise, yoga, psychotherapy, music therapy, etc.; the control group was conventional treatment groups or different non-pharmacological therapies compared with each other. The results of the report are required to include at least one of the following outcome indicators: The self-Rating Anxiety Scale (SAS), the Self-Rating Depression Scale (SDS), the Pittsburgh sleep quality index (PSQI), and effectiveness rate. The language of the publication is limited to Chinese or English.
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Pikilnyak, Andrey V., Nadia M. Stetsenko, Volodymyr P. Stetsenko, Tetiana V. Bondarenko, and Halyna V. Tkachuk. Comparative analysis of online dictionaries in the context of the digital transformation of education. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4431.

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The article is devoted to a comparative analysis of popular online dictionaries and an overview of the main tools of these resources to study a language. The use of dictionaries in learning a foreign language is an important step to understanding the language. The effectiveness of this process increases with the use of online dictionaries, which have a lot of tools for improving the educational process. Based on the Alexa Internet resource it was found the most popular online dictionaries: Cambridge Dictionary, Wordreference, Merriam–Webster, Wiktionary, TheFreeDictionary, Dictionary.com, Glosbe, Collins Dictionary, Longman Dictionary, Oxford Dictionary. As a result of the deep analysis of these online dictionaries, we found out they have the next standard functions like the word explanations, transcription, audio pronounce, semantic connections, and examples of use. In propose dictionaries, we also found out the additional tools of learning foreign languages (mostly English) that can be effective. In general, we described sixteen functions of the online platforms for learning that can be useful in learning a foreign language. We have compiled a comparison table based on the next functions: machine translation, multilingualism, a video of pronunciation, an image of a word, discussion, collaborative edit, the rank of words, hints, learning tools, thesaurus, paid services, sharing content, hyperlinks in a definition, registration, lists of words, mobile version, etc. Based on the additional tools of online dictionaries we created a diagram that shows the functionality of analyzed platforms.
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WANG, Xuesong, Xuliang SHI, Jing LV, Juncha ZHANG, Yongli HUO, Guang ZUO, Guangtong LU, Cunzhi LIU, and Yanfen SHE. Acupuncture and Related Therapies for anxiety and depression in Diarrhoea-Predominant Irritable Bowel Syndrome(IBS-D): A Network Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2022. http://dx.doi.org/10.37766/inplasy2022.3.0162.

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Анотація:
Review question / Objective: Acupuncture-related therapies are effective Diarrhoea-Predominant Irritable Bowel Syndrome(IBS-D), therefore, our aim was to evaluate and rank the effect of different acupuncture-related therapies for the anxiety-depression status of IBS-D patients. Eligibility criteria: The published randomized controlled trials (RCTs) of acupuncture-related therapies for the treatment of IBS-D, regardless of age and sex. Clear diagnostic criteria were required to confirm the diagnosis of IBS-D, Such as Rome I, Rome II, Rome III, Rome IV, and Chinese expert consensus. Interventions in the treatment group included various types of acupuncture-related therapies, including simple acupuncture (ACU), electroacupuncture (EA), warm acupuncture (WA), moxibustion (MOX), or a combination of acupuncture and drugs; the control group is anti-diarrheal or anti-spasmodic western medicine, or placebo, or comparison between various acupuncture-related therapies. The results of the report are required to include at least one of the following outcome indicators: (1) primary outcome: Hamilton anxiety rating scale( HAMA), hamilton depression rating scale(HAMD), self-rating anxiety scale (SAS), self-rating depression scale(SDS), secondary outcome: Response rate. The language of the publication was limited to Chinese or English.
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Nadiga, Balasubramanya T., and Daniel Livescu. Bayesian Analysis of RANS Models. Office of Scientific and Technical Information (OSTI), June 2016. http://dx.doi.org/10.2172/1257091.

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KHUDALOVA, M., V. FILONENKO, and E. KUDZOEVA. PSYCHOSOMATICS IN CONNECTION WITH THE AFFECTIVE DISORDERS OF PERSONALITY. Science and Innovation Center Publishing House, 2021. http://dx.doi.org/10.12731/2658-4034-2021-12-4-2-365-374.

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In recent years, we can notice a significant increase in psychosomatic disorders among borderline mental pathology, which are reasonably considered “the pathology of modern civilization”. The purpose of this study is to identify the relationship between psychosomatic disorders and the affective disorders of the personality. The study used the following methods: a diagnostic conversation and analysis of medical documents with the results of clinical examination, a scale for psychological express diagnostics of semi-structured depressive disorders (based on MMPI), a self-assessment scale by Ch.D. Spielberger - Yu.L. Hanin, Toronto Alexithymia Scale (TAS). Statistical methods of processing the empirical research results in the SPSS 22.0 program: descriptive statistics, correlation analysis (p-Spearman’s rank correlation). As a result of the study we can assert that psychosomatic disorders in respondents in the form of functional pathology of various organs and systems are connected with affective disorders in the form of moderate or severe depression of a neurotic level of various origins, alexithymia and high personal anxiety.
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Wooten, Hasani Omar. The ENDF/B-VIII.0 Compendium Volume IV: Radioactive Decay Rank-Order Analyses. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1617351.

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