Comparison of Two Entropy Spectral Analysis Methods for Streamflow Forecasting in Northwest China

Alzheimers disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain, causing a decline in cognitive abilities and difficulties in engaging in day-to-day activities. This study compares an FFT-based spectral analysis against a functional connectivity analysis based on phase synchronization, for finding known differences between AD patients and Healthy Control (HC) subjects. Both of these quantitative analysis methods were applied on a dataset comprising bipolar EEG montages values from 20 diagnosed AD patients and 20 age-matched HC subjects. Additionally, an attempt was made to localize the identified AD-induced brain activity effects in AD patients. The obtained results showed the advantage of the functional connectivity analysis method compared to a simple spectral analysis. Specifically, while spectral analysis could not find any significant differences between the AD and HC groups, the functional connectivity analysis showed statistically higher synchronization levels in the AD group in the lower frequency bands (delta and theta), suggesting that the AD patients brains are in a phase-locked state. Further comparison of functional connectivity between the homotopic regions confirmed that the traits of AD were localized in the centro-parietal and centro-temporal areas in the theta frequency band (4-8 Hz). The contribution of this study is that it applies a neural metric for Alzheimers detection from a data science perspective rather than from a neuroscience one. The study shows that the combination of bipolar derivations with phase synchronization yields similar results to comparable studies employing alternative analysis methods.

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Detecting cadmium contamination in loessal soils using near-infrared spectroscopy in the Xiaoqinling gold area

Energy & Environment ◽

10.1177/0958305x211030114 ◽

2021 ◽

pp. 0958305X2110301

Author(s):

Min Yang ◽

Youning Xu ◽

Haixing Shang ◽

Abdullah Abdullah ◽

Wen Zhang

Keyword(s):

Heavy Metal ◽

Spectral Analysis ◽

Near Infrared ◽

Heavy Metal Contamination ◽

Northwest China ◽

Peak Shift ◽

Hydroxyl Group ◽

Band Shift ◽

Heavy Metal Cations ◽

Field Reflectance

Loess is an important soil type that is widespread in the Loess Plateau of northwest China. However, mining exploitation, beneficiation, and metallurgy have led to inorganic contamination of soils that threatens the health of residents. The regular absorption peak shift of near-infrared (NIR) spectra in loessal soils represents a new method of soil environmental assessment based on field reflectance spectroscopy and hyperspectral remote sensing. Specifically, the NIR features of loessal soil will shift in response to changes in the soil composition and microstructure induced by heavy metal pollution. This study collected 27 samples from notable regions in the study area. Mid-infrared (MIR) spectral analysis, NIR spectral analysis, modified seven-step Tessier sequential extraction, and X-ray diffraction were used to analyze the band shift phenomenon of MIR and NIR features. The alignment of NIR bands was determined via the correlation between NIR and MIR bands associated with the vibration variations of the hydroxyl group. The correlations established by NIR band positions and exchangeable Cd cations were also analyzed. The results were then discussed according to the mineralogical characteristics of the heavy metal cations adsorbed on the surface and interlayer sites of clay minerals. These results can be used as a reference for the application of NIR technology to detecting heavy metal contamination in the soil of mining regions.

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A Strategic Application of Fast Fourier Transform as a Novel Tool to Evaluate the Extent of Neuronal Insult in Rat Model of Focal Cerebral Ischemia

Bangladesh Journal of Medical Physics ◽

10.3329/bjmp.v5i1.14666 ◽

2013 ◽

Vol 5 (1) ◽

pp. 29-36 ◽

Cited By ~ 3

Author(s):

S Paul ◽

P Bhattacharya ◽

AK Pandey ◽

N Sharma ◽

JP Tiwari ◽

...

Keyword(s):

Mathematical Modeling ◽

Animal Model ◽

Spectral Analysis ◽

Cerebral Ischemia ◽

Rat Brain ◽

Brain Function ◽

Focal Cerebral Ischemia ◽

Physiological State ◽

Analysis Methods ◽

Eeg Data

The present work envisages mathematical modeling of induced focal cerebral ischemia in animal model using EEG data with the help of Fast Fourier Transformation method. Amongst several analysis methods, spectral analysis methods are important because it detects the frequencies and characteristics changes of brain waveforms depending on the brain function affected from disorders and physiological state. There are many applications of FFT, and the most important being that it is one of the basic conventional spectral analysis methods. However, it has some limitations, for instance, it adds contributions in the low frequency region which are not present in the original signal, and necessitates the use of windowing for decreasing the error rate. The present analysis was undertaken to ensure actual correlation of the different mathematical paradigms. EEG data were obtained from different regions of rat brain and were processed by FFT modeling in MATLAB platform. The assessment of long lasting functional outcome and to prevalent classical approach to study stroke was necessitated and therefore highly recommended to evaluate the efficacy of therapeutic strategies in relation to EEG in animal model of brain stroke. This mathematical modeling specifically Power Spectrum Density analysis was done to correlate the different prevalent condition of rat brain function. DOI: http://dx.doi.org/10.3329/bjmp.v5i1.14666 Bangladesh Journal of Medical Physics Vol.5 No.1 2012 29-36

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