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2. The challenge with high permittivity acceptors in organic solar cells: a case study with Y-series derivativesPeter Fürk, Suman Mallick, Thomas Rath, Matiss Reinfelds, Mingjian Wu, Erdmann Spiecker, Nikola Simic, Georg Haberfehlner, Gerald Kothleitner, Barbara Ressel, Sarah Holler, Jana B. Schaubeder, Philipp Materna, Heinz Amenitsch, Gregor Trimmel, 2023, original scientific article Abstract: Y-series acceptors have brought a paradigm shift in terms of power conversion efficiencies of organic solar cells in the last few years. Despite their high performance, these acceptors still exhibit substantial energy loss, stemming from their low-permittivity nature. To tackle the energy loss situation, we prepared modified Y-series acceptors with improved permittivities via an alternative synthetic route.
Keywords: Solar cells, Y-series acceptors, morphology, efficiency measurements
Published in RUNG: 29.06.2023; Views: 563; Downloads: 3
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7. Time series analysis of duty cycle induced randomness in thermal lens systemSwapna Mohanachandran Nair Sindhu, 2020, original scientific article Abstract: The present work employs time series analysis, a proven powerful mathematical tool, for investigating the complex molecular dynamics of the thermal lens (TL) system induced by the duty cycle (C) variation. For intensity modulation, TL spectroscopy commonly uses optical choppers. The TL formation involves complex molecular dynamics that vary with the input photothermal energy, which is implemented by varying the duty cycle of the chopper. The molecular dynamics
is studied from the fractal dimension (D), phase portrait, sample entropy (S), and Hurst exponent (H) for different duty cycles. The increasing value of C is found to increase D and S, indicating that the system is becoming complex and less deterministic, as evidenced by the phase portrait analysis. The value of H less than 0.5 conforms the evolution of the TL system to more anti-persistent nature with C. The increasing value of C increases the enthalpy of the system that appears as an increase in full width at half maximum of the refractive index profile. Thus the study establishes that the sample entropy and thermodynamic entropy are directly related.
Keywords: Time series analysis
Fractal analysis
Photothermal lens spectroscopy
Fractal dimension
Hurst exponent
Sample entropy
Published in RUNG: 05.07.2022; Views: 823; Downloads: 0
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8. Phase Portrait for High Fidelity Feature Extraction and Classification: A Surrogate ApproachSwapna Mohanachandran Nair Sindhu, 2020, original scientific article Abstract: This paper proposes a novel surrogate method of classification of breath sound signals for auscultation through the principal component
analysis (PCA), extracting the features of a phase portrait. The nonlinear parameters of the phase portrait like the Lyapunov exponent, the
sample entropy, the fractal dimension, and the Hurst exponent help in understanding the degree of complexity arising due to the turbulence
of air molecules in the airways of the lungs. Thirty-nine breath sound signals of bronchial breath (BB) and pleural rub (PR) are studied
through spectral, fractal, and phase portrait analyses. The fast Fourier transform and wavelet analyses show a lesser number of high-intense,
low-frequency components in PR, unlike BB. The fractal dimension and sample entropy values for PR are, respectively, 1.772 and 1.041, while
those for BB are 1.801 and 1.331, respectively. This study reveals that the BB signal is more complex and random, as evidenced by the fractal
dimension and sample entropy values. The signals are classified by PCA based on the features extracted from the power spectral density (PSD)
data and the features of the phase portrait. The PCA based on the features of the phase portrait considers the temporal correlation of the signal
amplitudes and that based on the PSD data considers only the signal amplitudes, suggesting that the former method is better than the latter
as it reflects the multidimensional aspects of the signal. This appears in the PCA-based classification as 89.6% for BB, a higher variance than
the 80.5% for the PR signal, suggesting the higher fidelity of the phase portrait-based classification.
Keywords: Phase Portrait, time series, feature extraction, pleural rub
Published in RUNG: 05.07.2022; Views: 807; Downloads: 0
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10. Fractal and time-series analyses based rhonchi and bronchial auscultation: A machine learning approachSWAPNA MOHANACHANDRAN NAIR SINDHU SWAPNA,, 2022, original scientific article Abstract: Objectives: The present work reports the study of 34 rhonchi (RB) and
Bronchial Breath (BB) signals employing machine learning techniques, timefrequency, fractal, and non-linear time-series analyses. Methods: The timefrequency analyses and the complexity in the dynamics of airflow in BB
and RB are studied using both Power Spectral Density (PSD) features and
non-linear measures. For accurate prediction of these signals, PSD and nonlinear measures are fed as input attributes to various machine learning
models. Findings: The spectral analyses reveal fewer, low-intensity frequency
components along with its overtones in the intermittent and rapidly damping
RB signal. The complexity in the dynamics of airflow in BB and RB is investigated
through the fractal dimension, Hurst exponent, phase portrait, maximal
Lyapunov exponent, and sample entropy values. The greater value of entropy
for the RB signal provides an insight into the internal morphology of the airways
containing mucous and other obstructions. The Principal Component Analysis
(PCA) employs PSD features, and Linear Discriminant Analysis (LDA) along
with Pattern Recognition Neural Network (PRNN) uses non-linear measures
for predicting BB and RB. Signal classification based on phase portrait
features evaluates the multidimensional aspects of signal intensities, whereas
that based on PSD features considers mere signal intensities. The principal
components in PCA cover about 86.5% of the overall variance of the data class,
successfully distinguishing BB and RB signals. LDA and PRNN that use nonlinear time-series parameters identify and predict RB and BB signals with 100%
accuracy, sensitivity, specificity, and precision. Novelty: The study divulges the
potential of non-linear measures and PSD features in classifying these signals
enabling its application to be extended for low-cost, non-invasive COVID-19
detection and real-time health monitoring.
Keywords: lung signal, fractal analysis, sample entropy, nonlinear timeseries, machine learning techniques
Published in RUNG: 30.06.2022; Views: 967; Downloads: 0
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