Analysis of sea level fluctuations around the Australian coast with anomaly time series analysis approach

Thirty two-year observations (1990-2022) of water level from a total of 14 high-quality acoustic tide stations around the coast of Australia deployed for the Australian Baseline Sea Level Monitoring Project were analyzed. The following three approaches were used: Fourier Transform (FT), Empirical Mode Decomposition (EMD), and Singular Spectrum Analysis (SSA). The water level anomaly was observed to have predominant annual variations with a period of about 12 months based on the Fourier transform. The intrinsic components of stations were extracted in the EMD analysis and the mean period of each of the components was calculated using the zero down crossing method. A regular association was observed between the order of modes and the mean period such that the periods increase by a factor of two on successive modes. The third method used for anomaly analysis was SSA. The number of the obtained components in this method was less than in the EMD. Moreover, the order observed for the components' period in the EMD was not seen in this method. Spectral analysis of Autocorrelation function (ACF) has demonstrated that peak frequencies are almost the same with anomaly spectra so the dominant modes in anomalies are also present in the ACF.

State of the art in total pulse production in major states of India using ARIMA techniques

Pulses are staple protein-rich food for Indian vegetarians, and India is one of the largest producers in the world. The present investigation is an attempt to study the trend in the production of total pulses in India using the autoregressive integrated moving average (ARIMA) method. For stochastic trend estimation, yearly data were used for the period from 1961 to 2019. On the basis of the performance of several goodness of model fit criteria, the most suitable ARIMA model is chosen to capture the trend of pulse production. Forecasting for the 10 years from 2020 to 2029 is done, and it is observed that India has the highest forecast value (31.03302 million tonnes) in 2029. This study will play an important role in determining the gap between production of and demand for pulses in the future.

A novel spectral analysis method for distinguishing heavy metal stress of maize due to copper and lead: RDA and EMD-PSD

Monitoring heavy metal stress in crops via hyperspectral remote sensing technology is an effective way. A new approach, namely the ratio difference of autocorrelation function first derivative (RDA), is proposed to extract characteristic regions of maize leaves spectra for the initially identification on contaminated category of copper (Cu) and lead (Pb). Simultaneously, empirical mode decomposition (EMD) and power spectral density (PSD) are integrated to construct EMD-PSD to visually discrimination on Cu and Pb stress from frequency domain perspective. In our work, pot experiment contaminated by Cu and Pb were designed and carried out, and corresponding chemical data, chlorophyll and spectra of maize leaves were collected. Based on acquired spectra, RDA is used to obtain indicators and characteristic intervals of spectra, and then EMD-PSD is applied to obtain intrinsic mode functions (IMFs) from spectra and PSD maps. Through experimental analysis, the following conclusions are drawn: 1) the red edge and red shoulder region of spectra can be used as candidate on indicator to find the characteristic regions of spectra, and integrated intersection spectral intervals are considerable to distinguish Cu and Pb; 2) PSD maps extracted by EMD-PSD significantly can discriminate stress of Cu and Pb with three-dimensional visualization. This study takes the combination of spectral domain and frequency domain as the exploration point, the stress of Cu and Pb was distinguished by mutual verification based on spectra (group I and group II and 2014 experiment). In summary, the proposed method can identify the weak differences of spectra and distinguish the stress of Cu and Pb qualitatively, which provides a new perspective for the identification of heavy metal stress categories.

Spatial Image Correlation Spectroscopy (ICS): A Technique for Average Size Determination of Subcellular Accumulated Structures from Fluorescence Microscopic Images

Size determination of subcellular structures such as inclusion bodies (IBs) and granules from fluorescent images is important for identification and structural characterization. However, it is often time-consuming just for the comparison of the average size of the structures. Here, we introduce a high-throughput procedure to represent the average size of structures in fluorescent images using Spatial Image Correlation Spectroscopy (SICS). This procedure provides an easier comparison of bodies and granular structures such as inclusion bodies (IBs) including misfolded protein aggregation, granules containing RNA (e.g., stress granules and processing bodies).

A Simple Derivation of Diffusion Fluorescence Correlation Spectroscopy Equations

Since its introduction in the 1970s, Fluorescence Correlation Spectroscopy (FCS) has become a standard biophysical and physical chemistry tool to investigate not only a diffusion but also a broad range of biochemical processes including binding kinetics and anomalous diffusion. Since the derivation of FCS equations for many biochemical processes shares many common steps with the diffusion FCS equation, it is important to understand the mathematical theory behind the ​diffusion FCS equation. However, because the derivation of FCS equations requires advanced Fourier Transform and inverse Fourier Transform theory, which most biologists and biochemists are not familiar with, it is often treated as a black box in practice. In this study, we provide a simple and straightforward step-by-step derivation of FCS equations for free ​diffusion based on calculus-level mathematics, so that FCS equations and its applications can be accessible to a broad audience. Additionally, we compare our derivation with the conventional Fourier Transform and inverse Fourier Transform theory based approach.

Characterization and simulation of noise in PET images reconstructed with OSEM: Development of a method for the generation of synthetic images

INTRODUCTION

The goals of the study are to characterize imaging properties in 2D PET images reconstructed with the iterative algorithm ordered-subset expectation maximization (OSEM) and to propose a new method for the generation of synthetic images.

MATERIAL AND METHODS

The noise is analyzed in terms of its magnitude, spatial correlation, and spectral distribution through standard deviation, autocorrelation function, and noise power spectrum (NPS), respectively. Their variations with position and activity level are also analyzed. This noise analysis is based on phantom images acquired from ¹⁸F uniform distributions. Experimental recovery coefficients of hot spheres in different backgrounds are employed to study the spatial resolution of the system through point spread function (PSF). The NPS and PSF functions provide the baseline for the proposed simulation method: convolution with PSF as kernel and noise addition from NPS.

RESULTS

The noise spectral analysis shows that the main contribution is of random nature. It is also proven that attenuation correction does not alter noise texture but it modifies its magnitude. Finally, synthetic images of 2 phantoms, one of them an anatomical brain, are quantitatively compared with experimental images showing a good agreement in terms of pixel values and pixel correlations. Thus, the contrast to noise ratio for the biggest sphere in the NEMA IEC phantom is 10.7 for the synthetic image and 8.8 for the experimental image.

CONCLUSIONS

The properties of the analyzed OSEM-PET images can be described by NPS and PSF functions. Synthetic images, even anatomical ones, are successfully generated by the proposed method based on the NPS and PSF.

An efficient scheme for mental task classification utilizing reflection coefficients obtained from autocorrelation function of EEG signal

Classification of different mental tasks using electroencephalogram (EEG) signal plays an imperative part in various brain–computer interface (BCI) applications. In the design of BCI systems, features extracted from lower frequency bands of scalp-recorded EEG signals are generally considered to classify mental tasks and higher frequency bands are mostly ignored as noise. However, in this paper, it is demonstrated that high frequency components of EEG signal can provide accommodating data for enhancing the classification performance of the mental task-based BCI. Instead of using autoregressive (AR) parameters considering AR modeling of EEG data, reflection coefficients obtained from EEG signal are proposed as potential features. From a given frame of EEG data, reflection coefficients are directly extracted by using the autocorrelation values in a recursive fashion, which avoids matrix inversion and computation of AR parameters. Use of reflection coefficients not only provides an effective feature vector for EEG signal classification but also offers very low computational burden. Support vector machine classifier is deployed in leave-one-out cross-validation manner to carry out classification process. Extensive simulation is done on an openly accessible dataset containing five different mental tasks. It is found that the proposed scheme can classify mental tasks with a very high level of accuracy as well as low time complexity in contrast with some of the existing strategies.

Molecular dynamics simulations of NMR relaxation and diffusion of bulk hydrocarbons and water

Molecular dynamics (MD) simulations are used to investigate ¹H nuclear magnetic resonance (NMR) relaxation and diffusion of bulk n-C₅H₁₂ to n-C₁₇H₃₆ hydrocarbons and bulk water. The MD simulations of the ¹H NMR relaxation times T_1,2 in the fast motion regime where T₁=T₂ agree with measured (de-oxygenated) T₂ data at ambient conditions, without any adjustable parameters in the interpretation of the simulation data. Likewise, the translational diffusion D_T coefficients calculated using simulation configurations agree with measured diffusion data at ambient conditions. The agreement between the predicted and experimentally measured NMR relaxation times and diffusion coefficient also validate the forcefields used in the simulation. The molecular simulations naturally separate intramolecular from intermolecular dipole-dipole interactions helping bring new insight into the two NMR relaxation mechanisms as a function of molecular chain-length (i.e. carbon number). Comparison of the MD simulation results of the two relaxation mechanisms with traditional hard-sphere models used in interpreting NMR data reveals important limitations in the latter. With increasing chain length, there is substantial deviation in the molecular size inferred on the basis of the radius of gyration from simulation and the fitted hard-sphere radii required to rationalize the relaxation times. This deviation is characteristic of the local nature of the NMR measurement, one that is well-captured by molecular simulations.

Nonlinear least squares regression for single image scanning electron microscope signal-to-noise ratio estimation

A new method based on nonlinear least squares regression (NLLSR) is formulated to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. The estimation of SNR value based on NLLSR method is compared with the three existing methods of nearest neighbourhood, first-order interpolation and the combination of both nearest neighbourhood and first-order interpolation. Samples of SEM images with different textures, contrasts and edges were used to test the performance of NLLSR method in estimating the SNR values of the SEM images. It is shown that the NLLSR method is able to produce better estimation accuracy as compared to the other three existing methods. According to the SNR results obtained from the experiment, the NLLSR method is able to produce approximately less than 1% of SNR error difference as compared to the other three existing methods.

Categorisation of air quality monitoring stations by evaluation of PM(10) variability

Air Quality Monitoring Networks (AQMNs) are composed by a number of stations, which are typically classified as urban, suburban or rural, and background, industrial or traffic, depending on the location and the influence of the immediate surroundings. These categories are not necessarily homogeneous and distinct from one another, regarding the levels of the monitored pollutants. A classification providing groups with these features is of interest for air quality management and research purposes, and therefore, other classification criteria should be explored. In this work, the variations of PM10 concentrations in 43 stations in the AQMN of the Basque Country in the period 2005-2012 have been studied to group them according to common characteristics. The characteristic variations in time are synthesised by the autocorrelation function (ACF), with both daily and hourly data, and by the average diurnal evolution pattern of the normalised concentrations on a seasonal basis (Evol-P). A methodology based on k-means clustering of these features is proposed. Each classification gives a different piece of information that has been phenomenologically related with specific dispersion and emission dynamics. The classification based on Evol-Ps is found to be the most influential one when comparing PM10 levels between groups. A combination of these categorisations provides 5 groups with significantly different levels of PM10, improving the discrimination of the conventional classification. Our results indicate that the time series of the pollutant concentrations contain enough information to provide an objective classification of the monitoring stations in an AQMN.

Developmental nicotine exposure adversely effects respiratory patterning in the barbiturate anesthetized neonatal rat

Neonates at risk for sudden infant death syndrome (SIDS) are hospitalized for cardiorespiratory monitoring however, monitoring is costly and generates large quantities of averaged data that serve as poor predictors of infant risk. In this study we used a traditional autocorrelation function (ACF) testing its suitability as a tool to detect subtle alterations in respiratory patterning in vivo. We applied the ACF to chest wall motion tracings obtained from rat pups in the period corresponding to the mid-to-end of the third trimester of human pregnancy. Pups were drawn from two groups: nicotine-exposed and saline-exposed at each age (i.e., P7, P8, P9, and P10). Respiratory-related motions of the chest wall were recorded in room air and in response to an arousal stimulus (FIO2 14%). The autocorrelation function was used to determine measures of breathing rate and respiratory patterning. Unlike alternative tools such as Poincare plots that depict an averaged difference in a measure breath to breath, the ACF when applied to a digitized chest wall trace yields an instantaneous sample of data points that can be used to compare (data) points at the same time in the next breath or in any subsequent number of breaths. The moment-to-moment evaluation of chest wall motion detected subtle differences in respiratory pattern in rat pups exposed to nicotine in utero and aged matched saline-exposed peers. The ACF can be applied online as well as to existing data sets and requires comparatively short sampling windows (∼2 min). As shown here, the ACF could be used to identify factors that precipitate or minimize instability and thus, offers a quantitative measure of risk in vulnerable populations.