scholarly journals Radiolysis Generates a Complex Organosynthetic Chemical Network

2019 ◽  
Author(s):  
Zachary Adam ◽  
Albert C. Fahrenbach ◽  
Sofia Marie Jacobson ◽  
Betul Kacar ◽  
Dmitry Yu. Zubarev
Keyword(s):  
Closed Loop ◽  
High Reactivity ◽  
Self Organization ◽  
Biological Organization ◽  
Radical Species ◽  
Organizational Attributes ◽  
The Core ◽  
Heavy Tailed Distributions ◽  
Precursor Synthesis ◽  
Heavy Tailed

<p><a></a><a>Origins of life chemistry has progressed from seeking out the production of specific molecules to seeking out conditions in which macromolecular precursors may interact with one another in ways that lead to biological organization. Reported precursor synthesis networks generally lack biological organizational attributes. </a>Radical species are highly reactive, but do their chemical reaction networks resemble living systems? Here we report the results of radiolysis reaction experiments that connect abundant geochemical reservoirs to the production of carboxylic acids, amino acids, and ribonucleotide precursors and study the topological properties of the resulting network. The network exhibits attributes associated with biological systems: it is hierarchically organized, there are families of closed loop cycles, and the species and cycle histograms exhibit heterogeneous (heavy-tailed) distributions. The core cycles of the network are made possible by the high reactivity of radical species such as H and OH. Radiolysis is implicated as a unique prerequisite for driving abiotic organosynthetic self-organization. </p>

scholarly journals Radiolysis Generates a Complex Organosynthetic Chemical Network

2019 ◽  
Author(s):  
Zachary Adam ◽  
Albert C. Fahrenbach ◽  
Sofia Marie Jacobson ◽  
Betul Kacar ◽  
Dmitry Yu. Zubarev
Keyword(s):  
Closed Loop ◽  
High Reactivity ◽  
Self Organization ◽  
Biological Organization ◽  
Radical Species ◽  
Organizational Attributes ◽  
The Core ◽  
Heavy Tailed Distributions ◽  
Precursor Synthesis ◽  
Heavy Tailed

<p><a></a><a>Origins of life chemistry has progressed from seeking out the production of specific molecules to seeking out conditions in which macromolecular precursors may interact with one another in ways that lead to biological organization. Reported precursor synthesis networks generally lack biological organizational attributes. </a>Radical species are highly reactive, but do their chemical reaction networks resemble living systems? Here we report the results of radiolysis reaction experiments that connect abundant geochemical reservoirs to the production of carboxylic acids, amino acids, and ribonucleotide precursors and study the topological properties of the resulting network. The network exhibits attributes associated with biological systems: it is hierarchically organized, there are families of closed loop cycles, and the species and cycle histograms exhibit heterogeneous (heavy-tailed) distributions. The core cycles of the network are made possible by the high reactivity of radical species such as H and OH. Radiolysis is implicated as a unique prerequisite for driving abiotic organosynthetic self-organization. </p>

Probability and Random Processes

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Random Processes ◽  
Distribution Functions ◽  
Basic Logic ◽  
Heavy Tailed Distributions ◽  
Random Components ◽  
Classical Distribution ◽  
Heavy Tailed ◽  
Basic Facts ◽  
Stochastic Events ◽  
Stochastic Event

Phenomena, systems, and processes are rarely purely deterministic, but contain stochastic,probabilistic, or random components. For that reason, a probabilistic descriptionof most phenomena is necessary. Probability theory provides us with the tools for thistask. Here, we provide a crash course on the most important notions of probabilityand random processes, such as odds, probability, expectation, variance, and so on. Wedescribe the most elementary stochastic event—the trial—and develop the notion of urnmodels. We discuss basic facts about random variables and the elementary operationsthat can be performed on them. We learn how to compose simple stochastic processesfrom elementary stochastic events, and discuss random processes as temporal sequencesof trials, such as Bernoulli and Markov processes. We touch upon the basic logic ofBayesian reasoning. We discuss a number of classical distribution functions, includingpower laws and other fat- or heavy-tailed distributions.

scholarly journals A Method for Confidence Intervals of High Quantiles

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 70
Author(s):  
Mei Ling Huang ◽  
Xiang Raney-Yan
Keyword(s):  
Confidence Intervals ◽  
Computational Algorithm ◽  
Geometric Mean ◽  
Asymptotic Properties ◽  
Quantile Estimation ◽  
Heavy Tailed Distributions ◽  
High Quantiles ◽  
High Quantile ◽  
Heavy Tailed ◽  
Infinite Moments

The high quantile estimation of heavy tailed distributions has many important applications. There are theoretical difficulties in studying heavy tailed distributions since they often have infinite moments. There are also bias issues with the existing methods of confidence intervals (CIs) of high quantiles. This paper proposes a new estimator for high quantiles based on the geometric mean. The new estimator has good asymptotic properties as well as it provides a computational algorithm for estimating confidence intervals of high quantiles. The new estimator avoids difficulties, improves efficiency and reduces bias. Comparisons of efficiencies and biases of the new estimator relative to existing estimators are studied. The theoretical are confirmed through Monte Carlo simulations. Finally, the applications on two real-world examples are provided.

scholarly journals Influence of Salt on the Self-Organization in Solutions of Star-Shaped Poly-2-alkyl-2-oxazoline and Poly-2-alkyl-2-oxazine on Heating

Polymers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1152
Author(s):  
Tatyana Kirila ◽  
Anna Smirnova ◽  
Alla Razina ◽  
Andrey Tenkovtsev ◽  
Alexander Filippov
Keyword(s):  
Phase Separation ◽  
Salt Concentration ◽  
Monomer Unit ◽  
Salt Content ◽  
The Self ◽  
Self Organization ◽  
Phase Separation Temperature ◽  
The Core ◽  
Separation Temperature ◽  
Water Salt

The water–salt solutions of star-shaped six-arm poly-2-alkyl-2-oxazines and poly-2-alkyl-2-oxazolines were studied by light scattering and turbidimetry. The core was hexaaza[26]orthoparacyclophane and the arms were poly-2-ethyl-2-oxazine, poly-2-isopropyl-2-oxazine, poly-2-ethyl-2-oxazoline, and poly-2-isopropyl-2-oxazoline. NaCl and N-methylpyridinium p-toluenesulfonate were used as salts. Their concentration varied from 0–0.154 M. On heating, a phase transition was observed in all studied solutions. It was found that the effect of salt on the thermosensitivity of the investigated stars depends on the structure of the salt and polymer and on the salt content in the solution. The phase separation temperature decreased with an increase in the hydrophobicity of the polymers, which is caused by both a growth of the side radical size and an elongation of the monomer unit. For NaCl solutions, the phase separation temperature monotonically decreased with growth of salt concentration. In solutions with methylpyridinium p-toluenesulfonate, the dependence of the phase separation temperature on the salt concentration was non-monotonic with minimum at salt concentration corresponding to one salt molecule per one arm of a polymer star. Poly-2-alkyl-2-oxazine and poly-2-alkyl-2-oxazoline stars with a hexaaza[26]orthoparacyclophane core are more sensitive to the presence of salt in solution than the similar stars with a calix[n]arene branching center.

scholarly journals Optimal Randomness for Stochastic Configuration Network (SCN) with Heavy-Tailed Distributions

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 56
Author(s):  
Haoyu Niu ◽  
Jiamin Wei ◽  
YangQuan Chen
Keyword(s):  
Research Work ◽  
Cauchy Distribution ◽  
Classification Model ◽  
Test Accuracy ◽  
Functional Link ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Improved Performance ◽  
Hidden Layer ◽  
Hidden Nodes

Stochastic Configuration Network (SCN) has a powerful capability for regression and classification analysis. Traditionally, it is quite challenging to correctly determine an appropriate architecture for a neural network so that the trained model can achieve excellent performance for both learning and generalization. Compared with the known randomized learning algorithms for single hidden layer feed-forward neural networks, such as Randomized Radial Basis Function (RBF) Networks and Random Vector Functional-link (RVFL), the SCN randomly assigns the input weights and biases of the hidden nodes in a supervisory mechanism. Since the parameters in the hidden layers are randomly generated in uniform distribution, hypothetically, there is optimal randomness. Heavy-tailed distribution has shown optimal randomness in an unknown environment for finding some targets. Therefore, in this research, the authors used heavy-tailed distributions to randomly initialize weights and biases to see if the new SCN models can achieve better performance than the original SCN. Heavy-tailed distributions, such as Lévy distribution, Cauchy distribution, and Weibull distribution, have been used. Since some mixed distributions show heavy-tailed properties, the mixed Gaussian and Laplace distributions were also studied in this research work. Experimental results showed improved performance for SCN with heavy-tailed distributions. For the regression model, SCN-Lévy, SCN-Mixture, SCN-Cauchy, and SCN-Weibull used less hidden nodes to achieve similar performance with SCN. For the classification model, SCN-Mixture, SCN-Lévy, and SCN-Cauchy have higher test accuracy of 91.5%, 91.7% and 92.4%, respectively. Both are higher than the test accuracy of the original SCN.

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