Chaos and order in stateless societies: Intercommunity exchange as a factor impacting the population dynamical patterns

The once abstract notions of dynamical chaos now appear naturally in various systems [Kaplan D, Glass L. Understanding nonlinear dynamics. New York: Springer; 1995]. As a result, future trajectories of the systems may be difficult to predict. In this paper, we demonstrate the appearance of chaotic dynamics in model human communities, which consist of producers of agricultural product and producers of agricultural equipment. In the case of a solitary community, the horizon of predictability of the human population dynamics is shown to be dependent on both intrinsic instability of the dynamics and the chaotic attractor sizes. Since a separate community is usually a part of a larger commonality, we study the dynamics of social systems consisting of two interacting communities. We show that intercommunity barter can lead to stabilization of the dynamics in one of the communities, which implies persistence of stable equilibrium under changes of the maximum value of the human population growth rate. However, in the neighboring community, the equilibrium turns into a stable limit cycle as the maximum value of the human population growth rate increases. Following an increase in the maximum value of the human population growth rate leads to period-doubling bifurcations resulting in chaotic dynamics. The horizon of predictability of the chaotic oscillations is found to be limited by 5 years. We demonstrate that the intercommunity interaction can lead to the appearance of long-period harmonics in the chaotic time series. The period of the harmonics is of order 100 and 1000 years. Hence the long-period changes in the population size may be considered as an intrinsic feature of the human population dynamics.     

A novel generation of photoactive comb‐shaped polyamides for the photoalignment of liquid crystals

A new approach to the synthesis of photoactive comb‐shaped homo‐ and copolyamides containing azobenzene, cinnamate, and coumarin side groups for photoalignment of liquid crystals was elaborated. Photooptical properties and photoorientational ability of these polymers with respect to liquid crystals were studied. It was shown that polarized UV irradiation of all spin‐coated polyamides leads to orientation of liquid crystalline molecules deposited on the polyamide thin films. The synthesized polymers containing cinnamate and coumarin side groups as well as azobenzene‐containing cyano‐ and nitro‐substituted polymers demonstrated good orientation ability in relation to liquid crystals displaying photoinduced planar orientation with high dichroism values within the range of 0.68–0.72. Contrary to the above‐mentioned polyamides, azobenzene‐containing fluorosubstituted polymers induced a homeotropic orientation of liquid crystals. It was shown that the synthesized photoactive polyamides can be considered as promising photoalignment materials for application in display technology, photonics, and other “smart” optical devices. © 2013 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2013 , 51, 4031–4041     

Palladium-catalyzed Suzuki cross-coupling reactions in a microemulsion

An enhancement of palladium-catalyzed Suzuki cross-coupling reactions between substrates possessing long-chain alkyl or oxyalkyl substituents in two-phase media, in the presence of sodium dodecylsulfate, is shown. Quantitative evaluations of the influence of SDS on the reaction rates were determined.     

Novel types of boronated chlorin e6 conjugates via ‘click chemistry’

Conjugates of chlorin e₆ with closo‐dodecaborate and cobalt bis(dicarbollide) anions were synthesized for the first time in high yields using the advanced ‘click’ methodology. In vitro study on A549 human lung adenocarcinoma cells revealed that the synthesized boronated conjugates are able to penetrate and accumulate in cancer cells, but their intracellular concentration is not sufficient for effective photodynamic and boron neutron capture therapy of cancer. Copyright © 2009 John Wiley & Sons, Ltd.     

ML-Based Analysis of Particle Distributions in High-Intensity Laser Experiments: Role of Binning Strategy

When entering the phase of big data processing and statistical inferences in experimental physics, the efficient use of machine learning methods may require optimal data preprocessing methods and, in particular, optimal balance between details and noise. In experimental studies of strong-field quantum electrodynamics with intense lasers, this balance concerns data binning for the observed distributions of particles and photons. Here we analyze the aspect of binning with respect to different machine learning methods (Support Vector Machine (SVM), Gradient Boosting Trees (GBT), Fully-Connected Neural Network (FCNN), Convolutional Neural Network (CNN)) using numerical simulations that mimic expected properties of upcoming experiments. We see that binning can crucially affect the performance of SVM and GBT, and, to a less extent, FCNN and CNN. This can be interpreted as the latter methods being able to effectively learn the optimal binning, discarding unnecessary information. Nevertheless, given limited training sets, the results indicate that the efficiency can be increased by optimizing the binning scale along with other hyperparameters. We present specific measurements of accuracy that can be useful for planning of experiments in the specified research area.