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Compatible Photochromic Systems for Opto-electronic Applications.

ABSTRACT: Today, a lot of attention is paid to remote controlled opto-electronic devices. Many of them are commonly used in the society, industry, and science. Accessories dedicated to the particular utilization are desired. The point is to find a simple way to obtain smart and functional appliances. Materials engineering faces such problems and provides a variety of solutions concerning advanced material design, preparation, and utilization. Photochromic materials represent one of the already known materials, which still find other objectives in new fields of life. In our work, we present two differently constructed photoresponsive polymers, which give significantly different nonlinear optical (NLO) response visible as noticeable optical signal modulation. By playing with diversified laser light energy or its frequency, NLO output characterized appealing, and individual characteristics (doubled Δn ∼0.02 vs 0.04 and entirely different kinetics for two similar materials and the same laser pumping). Interestingly, high output signal repeatability and stability were achieved, which indicate the investigated materials as promising candidates in the construction of various opto-electronic devices. Additionally, a set of photoresponsive studies, reflectometry, and theoretical insights was performed and included in this work.

SUBMITTER: Szukalski A

PROVIDER: S-EPMC8686115 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Square (OLS) estimation, a viable alternative is to use Artificial Neural Networks (ANNs) implemented in a simple structure with one input and one output layer and trained in a way such that the weights matrix corresponds to the matrix of VAR parameters. In this work, we introduce an ANN combined with SS models for the computation of GC. The ANN is trained through the Stochastic Gradient Descent L1 (SGD-L1) algorithm, and a cumulative penalty inspired from penalized regression is applied to the network weights to encourage sparsity. Simulating networks of coupled Gaussian systems, we show how the combination of ANNs and SGD-L1 allows to mitigate the strong reduction in accuracy of OLS identification in settings of low ratio between number of time series points and of VAR parameters. We also report how the performances in GC estimation are influenced by the number of iterations of gradient descent and by the learning rate used for training the ANN. We recommend using some specific combinations for these parameters to optimize the performance of GC estimation. Then, the performances of ANN and OLS are compared in terms of GC magnitude and statistical significance to highlight the potential of the new approach to reconstruct causal coupling strength and network topology even in challenging conditions of data paucity. The results highlight the importance of of a proper selection of regularization parameter which determines the degree of sparsity in the estimated network. Furthermore, we apply the two approaches to real data scenarios, to study the physiological network of brain and peripheral interactions in humans under different conditions of rest and mental stress, and the effects of the newly emerged concept of remote synchronization on the information exchanged in a ring of electronic oscillators. The results highlight how ANNs provide a mesoscopic description of the information exchanged in networks of multiple interacting physiological systems, preserving the most active causal interactions between cardiovascular, respiratory and brain systems. Moreover, ANNs can reconstruct the flow of directed information in a ring of oscillators whose statistical properties can be related to those of physiological networks.

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Compatible Photochromic Systems for Opto-electronic Applications.

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