scholarly journals Entropy Measures for Data Analysis: Theory, Algorithms and Applications

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 935
Author(s):  
Karsten Keller
Keyword(s):  
Data Analysis ◽  
Analysis Theory ◽  
Entropy Measures ◽  
Non Linear

Entropies and entropy-like quantities are playing an increasing role in modern non-linear data analysis and beyond [...]

scholarly journals Entropy Measures for Data Analysis II: Theory, Algorithms and Applications

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1496
Author(s):  
Karsten Keller
Keyword(s):  
Data Analysis ◽  
Entropy Measures ◽  
Non Linear

Entropies and entropy-like quantities are playing an increasing role in modern non-linear data analysis [...]

Innovative graphical-numerical methods to investigate compositional changes in groundwater systems

2020 ◽  
Author(s):  
Roberta Sauro Graziano ◽  
Renguang Zuo ◽  
Antonella Buccianti ◽  
Orlando Vaselli ◽  
Barbara Nisi ◽  
...  
Keyword(s):  
Data Analysis ◽  
Numerical Methods ◽  
Compositional Data ◽  
Spatial Behavior ◽  
Cumulative Distribution ◽  
Geochemical Data ◽  
Compositional Data Analysis ◽  
Analysis Theory ◽  
Non Linear ◽  
Groundwater Systems

<p>Groundwater systems are typical dissipative structures and their evolution can be affected by non-linear dynamics. In this framework, geochemical and hydrological processes are often characterized by random components mixed with intermittency and presence of positive feedbacks between fluid transport and mineral dissolution. Therefore, in these cases, complex variability structures in the chemical signature of waters are recognized. Large fluctuations in intermittent processes are not rare as in normal and log-normal processes and significantly contribute to the statistical moments, thus moving the physicochemical data from the Euclidean geometry to fractals and multifractals.</p><p>Since the knowledge of dynamics in water systems has substantial implications in the management of the water resource, groundwater chemistry can better be understood by using innovative graphical and numerical methods in the light of the Compositional Data Analysis Theory (CoDA, Aitchison, 1986), which is particularly suitable to explore the whole composition and the relationships between its parts.</p><p>The whole compositional change, characterizing each sample with respect to some end-members (i.e. rain waters, pristine waters and sea water), is modeled by using the perturbation operator in the simplex geometry (Pawlowsky-Glahn and Buccianti, 2011). Perturbation factors are calculated and then analyzed by investigating their cumulative distribution function (Pr[X>=x]) with the aim of registering the presence of power laws (fractal and multifractal dynamics) and forecasting a possible spatial behavior.</p><p>Results obtained for some aquifers from Tuscany (central Italy) are presented and discussed in the framework of the GEOBASI project (Nisi et al., 2016). Preliminary evaluations indicate that perturbation factors are sensible tools to: 1) identify the different components (random, deterministic, fractal) contributing to the variability of the geochemical data, 2) discriminate the role of additive and multiplicative phenomena in time and/or space, 3) highlight the presence of non-linear dissipation with the energy exchanges between different scales.[Office1] </p><p> </p><p>Aitchison, J., 1986.  The statistical analysis of compositional data. Monographs on Statistics and Applied Probability (Reprinted in 2003 by The Blackburn Press), Chapman and Hall, 416 p.</p><p>Nisi, B., Buccianti, A., Raco, B., Battaglini, R., 2016. Analysis of complex regional databases and their support in the identification of background/baseline compositional facies in groundwater investigation: developments and application examples. Journal of Geochemical Exploration 164, 3-17</p><p>Pawlowsky-Glahn, V., Buccianti, A., 2011. Compositional Data Analysis: Theory and applications. Chichester, John Wiley & Sons, 378 p.</p>

Ridgetail White Prawn (Exopalaemon carinicauda) K Value Predicting Method by Using Electronic Nose Combined with Non-linear Data Analysis Model

2018 ◽  
Vol 11 (11) ◽  
pp. 3121-3129 ◽  
Author(s):  
Chenning Shao ◽  
Haonan Zheng ◽  
Zhixin Zhou ◽  
Jian Li ◽  
Xiongwei Lou ◽  
...  
Keyword(s):  
Data Analysis ◽  
Electronic Nose ◽  
Exopalaemon Carinicauda ◽  
Analysis Model ◽  
K Value ◽  
Non Linear

Metabolomics signature in serum months to years before thoracic cancer: The HUNT study.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e23059-e23059
Author(s):  
Oluf D. Røe ◽  
Vincenzo Lagani ◽  
Hans Fredrik Kvitvang ◽  
Maria Markaki ◽  
Ioannis Tsamardinos ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Data Analysis ◽  
Cross Validation ◽  
Performance Estimation ◽  
Serum Samples ◽  
Analysis Pipeline ◽  
Early Cancer Diagnosis ◽  
Non Linear ◽  
Hunt Study ◽  
Data Analysis Pipeline

e23059 Background: The Cancer-Biomarkers in HUNTinitiative seeks to identify novel biomarkers for the early cancer diagnosis. For lung cancers and mesothelioma clinically useful early markers are not available. In the prospective HUNT study in Norway, pre-diagnostic samples ranging 0-20 years before diagnosis are available for research purposes. Here we present our first results on high-throughput metabolomics analysis in serum two months to 16 years before diagnosis. Methods: LC-MS untargeted (Amide-) metabolites (n = 1042) were profiled in serum samples from 48 future patients (12 each of adeno-, squamous cell carcinoma, small-cell lung cancer and mesothelioma) and from 48 controls that were cancer-free 5 years after blood sampling. All were active smokers. Metabolic features for (a) each cancer and (b) all cancers pooled together were analyzed with moderated t-test (R limma package). Multivariate analyses included (a) OPLS-DA and (b) signature identification through a data-analysis pipeline that includes feature selection (such as the algorithm in [1]), non-linear modelers (e.g., Random Forests) and Cross-Validation with bootstrapping [2] for optimizing algorithms and providing unbiased performance estimation. The pipeline is implemented in the Just Add Data software (Gnosis Data Analysis). Results: Univariate and OPLS-DA analyses did not identify any association between metabolites and cancer. The non-linear data analysis pipeline identified a signature containing five metabolites able to discriminate between cancer and non-cancer patients, statistically significantly better than random (AUC = 0.667, CI = [0.536, 0.784]). Conclusions: Our results indicate that metabolic profiling in serum may help in identifying subjects who are likely to be diagnosed with lung cancer/mesothelioma in a time period of several years before diagnosis. More data will be presented at the annual meeting. Further validation studies are planned for confirming the replicability of these findings. 1) Lagani V et al., 2016. arXiv:1611.03227 2) Greasidou L, 2017. Bias Correction of the Cross-Validation Performance Estimate and Speed Up of its Execution Time, MSc Thesis, University of Crete

Non-linear kinetics of ethanol elimination in man: Medico-legal applications of the terminal concentration-time data analysis

1983 ◽  
Vol 21 (3) ◽  
pp. 207-213 ◽  
Author(s):  
R. Bruno ◽  
A. Iliadis ◽  
M.J. Treffot ◽  
B. Mariotti ◽  
J.P. Cano ◽  
...  
Keyword(s):  
Data Analysis ◽  
Time Data ◽  
Concentration Time ◽  
Linear Kinetics ◽  
Non Linear ◽  
Ethanol Elimination ◽  
Kinetics Of
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