Fourier Transformation Of Data Research Articles

Synthesis, Characterization, and Theoretical Calculations of a Novel Organic Corrosion Inhibitor for Mild Steel

Q235 is a carbon structural steel sheet that is widely used in the construction and engineering sectors due to its cost-effectiveness and durability. Here, a novel organic corrosion inhibitor for a Q235 sheet based on furan-thiadiazole (5-amino-2-furyl-1, 3, 4-thiadiazole, AFTZ) has been designed, successfully synthesized, and characterized in this work. Results of experimental data and theoretical calculations indicated that AFTZ has an excellent corrosion inhibition property for a Q235 sheet in 1.0 mol/L H2SO4. The highest efficiency of corrosion inhibitor tested by a weight loss experiment can reach 91.17% and an electrochemical experiment was up to 95.85% when AFTZ increased to 2 mg/mL in H2SO4 solution. The morphological characteristics of Q235 sheets with or without corrosion inhibitor showed that the target inhibitor has good corrosion inhibition performance. Quantum computation using the density functional theory method of Gaussian09 software and Fourier-transform infrared data attribute the excellent corrosion inhibition to the film-forming nature of AFTZ.

Chatter detection in simulated machining data: a simple refined approach to vibration data

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

Exploring the determinants of organic matter bioavailability through substrate-explicit thermodynamic modeling

Microbial decomposition of organic matter (OM) in river corridors is a major driver of nutrient and energy cycles in natural ecosystems. Recent advances in omics technologies enabled high-throughput generation of molecular data that could be used to inform biogeochemical models. With ultrahigh-resolution OM data becoming more readily available, in particular, the substrate-explicit thermodynamic modeling (SXTM) has emerged as a promising approach due to its ability to predict OM degradation and respiration rates from chemical formulae of compounds. This model implicitly assumes that all detected organic compounds are bioavailable, and that aerobic respiration is driven solely by thermodynamics. Despite promising demonstrations in previous studies, these assumptions may not be universally valid because OM degradation is a complex process governed by multiple factors. To identify key drivers of OM respiration, we performed a comprehensive analysis of diverse river systems using Fourier-transform ion cyclotron resonance mass spectrometry OM data and associated respiration measurements collected by the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium. In support of our argument, we found that the incorporation of all compounds detected in the samples into the SXTM resulted in a poor correlation between the predicted and measured respiration rates. The data-model consistency was significantly improved by the selective use of a small subset (i.e., only about 5%) of organic compounds identified using an optimization method. Through a subsequent comparative analysis of the subset of compounds (which we presume as bioavailable) against the full set of compounds, we identified three major traits that potentially determine OM bioavailability, including: (1) thermodynamic favorability of aerobic respiration, (2) the number of C atoms contained in compounds, and (2) carbon/nitrogen (C/N) ratio. We found that all three factors serve as “filters” in that the compounds with undesirable properties in any of these traits are strictly excluded from the bioavailable fraction. This work highlights the importance of accounting for the complex interplay among multiple key traits to increase the predictive power of biogeochemical and ecosystem models.

Comparison of three cyclodextrins to optimize bisphenol A extraction from source water: Computational, spectroscopic, and analytical studies.

Computationally and spectroscopically assisted analytical comparative investigation into the extraction of bisphenol A using three cyclodextrins, that is, α, β, and γ respectively, were performed. A simple, self-tailored μ-solid-phase extraction podium was used to extract bisphenol A from water samples, and high-performance liquid chromatography-ultraviolet was used for the qualitative and quantitative analysis of bisphenol A. Density functional theory first principle calculations, attenuated total reflectance Fourier-transform infrared spectroscopy and Fourier-transform Raman spectroscopy data supports the analytical selection of β-cyclodextrin as the adsorbent for bisphenol A extraction. Analytical optimization of various parameters including sample volume, sample pH, eluting solvent and its volume was performed to discover the most proper conditions for maximum extraction. Under the optimized conditions, a limit of detection value of 0.70ng/ml and a limit of quantification value of 2.31ng/ml was achieved with β-cyclodextrin, with recovery (%) values over 98.40-102.50 in real source water samples. Overall, well assisted by comprehensive computational and spectroscopic studies, a novel, simple, sensitive and economic analytical method was developed for the extraction of bisphenol A from source water using cyclodextrin.

Kinetic and molecular evidence for DON transformation in the deep vadose zone: Important implications for soil nitrogen budgeting and groundwater nitrate management

The neglect of dissolved organic nitrogen (DON), especially with regard to its transformation in the deep vadose zone, can result in high uncertainty surrounding estimated nitrogen (N) leaching losses to groundwater. This study provided kinetic and molecular evidence for DON transformation in the deep vadose zone by focusing on a typical agricultural area in Eastern China. DON was as important as inorganic N for N stocks, and 28.04% ∼ 71.94% of DON was sealed in the deep vadose zones (i.e., > 1 m depth). Soil N mineralization rates gradually decreased as incubation proceeded, and aerobic condition was more favorable for soil N mineralization. Both shallow and deep vadose zones could be potential hotspots for N mineralization, and the trend of N mineralization rate varying with soil depth was site-specific. Fourier-transform ion cyclotron resonance mass spectrometry data revealed that DON was dominated by lipids before incubation, whereas lignins were dominant after incubation was complete. Lignin-like compounds were not intrinsically recalcitrant, instead persisting in soil due to concurrent removal, transformation, and formation. DON transformation occurred along the entire soil profile regardless of redox conditions, potentially having different microbe-mediated pathways. These findings emphasize the implications of considering DON transformation in the deep vadose zone for soil N budgeting and groundwater nitrate management.

Pattern Recognition for Ultraviolet and Fourier Transform Data: A Walkthrough of Techniques and Direction

Pattern recognition has been a thriving field of research in many applications, particularly spectral data classification requiring vast, complex, and high-dimensional data. It aims to extract patterns from data and distinguish the acquired data in order to create a new type of description and pattern. This study walks over pattern identification algorithms for spectral data, namely Ultraviolet (UV) and Fourier Transform Infrared (FTIR) especially within the past five years. In addition, this article will address the present trend analysis, obstacles, and future methods for the pattern identification field of research, with a specific emphasis on UV and FTIR spectroscopic data.

Bayesian Operational Modal Analysis with Genetic Optimization for Structural Health Monitoring of the Long-Span Bridge

Modal parameters, including natural frequencies, damping ratios, and mode shapes, are vital for evaluating the in-service condition of long-span bridges subjected to performance degradation or damages. Operational modal identification techniques requiring only ambient vibration responses are usually utilized to identify the modal parameters of in-service bridges. Due to the measurement incompleteness and modeling errors, uncertainties would inevitably be involved in the identification. The changing operational loads of long-span bridges, e.g. vehicles and winds, could also increase uncertainties. These uncertainty factors could decrease the precision of the operational modal identification and undermine the reliability of structural health monitoring (SHM) for long-span bridges. This study introduces an improved Bayesian modal identification approach using the scaled Fast Fourier Transform data for uncertainty quantification. The proposed method integrates the genetic optimization with the posterior probability density function of modal parameters to ensure the accuracy and robustness of the iteration process. Additionally, an asymptotic estimate interval with the assumption of a high signal-to-noise ratio is defined to improve the computational efficiency. The acceleration responses from a numerical example and a full-scale long-span bridge are adopted to validate the performance of the proposed method. Results show that the improved Bayesian approach can accurately identify the modal parameters and efficiently quantify the uncertainties. The method enhances the reliability of modal tracking for SHM of operational long-span bridges.

Aerodynamic Parameter Estimation Using Reconstructed Turbulence Measurements

A classical flow-vane approach for reconstructing translational gust velocities from air data and inertial sensor measurements was improved and implemented for real-time computation. The reconstructed turbulence measurements were then included in a system identification analysis to estimate nondimensional stability and control derivatives in a longitudinal short period model using the maximum likelihood equation-error method in the frequency domain with Fourier-transform data. Flight-test results using 44 maneuvers from a subscale transport-type airplane showed that the power spectra for the reconstructed vertical gusts resembled standard Dryden or von Kármán turbulence models. Flight data in moderate and severe turbulence exhibited a decorrelation of the modeling data. In particular, pitch rate and angle-of-attack rate derivatives could both be identified from flight data about straight and level flight without special maneuvers or prior information.

A Non-Iterative Crosswords-Inspired Approach to the Recovery of 2-D Discrete Signals From Phaseless Fourier Transform Data

We propose an innovative approach to the phase retrieval of 2-D discrete complex signals. The solution strategy profitably exploits some fundamental results available for the phase retrieval of one-dimensional discrete signals by following a simple yet effective philosophy resembling the solution of crossword puzzles. The resulting procedure is completely deterministic and allows identifying all the solutions of the problem by using just the spectral amplitude data, knowledge of the support of the source, and a few additional information. As a distinguishing characteristic, it does not exploit neither global-optimization algorithms nor iterative techniques. Numerical examples witnessing the interest of the proposed ideas are given for antenna applications.

Improved Understanding of Dissolved Organic Matter Processing in Freshwater Using Complementary Experimental and Machine Learning Approaches.

Dissolved organic matter plays an important role in aquatic ecosystems and poses a major problem for drinking water production. However, our understanding of DOM reactivity in natural systems is hampered by its complex molecular composition. Here, we used Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and data from two independent studies to disentangle DOM reactivity based on photochemical and microbial-induced transformations. Robust correlations of FT-ICR-MS peak intensities with chlorophyll a and solar irradiation were used to define 9 reactivity classes for 1277 common molecular formulas. Germany's largest drinking water reservoir was sampled for 1 year, and DOM processing in stratified surface waters could be attributed to photochemical transformations during summer months. Microbial DOM alterations could be distinguished based on correlation coefficients with chlorophyll a and often shared molecular features (elemental ratios and mass) with photoreactive compounds. In particular, many photoproducts and some microbial products were identified as potential precursors of disinfection byproducts. Molecular DOM features were used to further predict molecular reactivity for the remaining compounds in the data set based on a random forest model. Our method offers an expandable classification approach to integrate the reactivity of DOM from specific environments and link it to molecular properties and chemistry.

A mode shape assembly algorithm by using two stage Bayesian Fast Fourier Transform Approach

Abstract Operational modal analysis may require identifying global modal shapes by using multiple setup measurements. For this purpose, various algorithms have been developed which make use of the Bayesian approach to estimate the global mode shapes. The main motivation of the available Bayesian approaches is based on the estimation of the optimal global mode shape vector directly from Fast Fourier Transform data or assembling the local mode shapes that are identified in the individual setups by using Gaussian approximation. In this study, the two-stage Bayesian Fast Fourier Transform Approach which is originally applied to single setups is implemented to multiple setup problems for well separated modes. Analytically it is shown that the resulting formulation is the same for the mode shape assembly by using the Gaussian approximation. In addition, the weights of individual setups in the global mode shape vector is analytically calculated which depend on the Hessian matrix for local mode shapes. To validate the proposed methodology, a numerical example that considers setup-to-setup variability of modal signal-noise ratios is presented. For comparison purposes a ten-story shear frame model is experimentally investigated, and the measurements of a benchmark bridge structure are considered in the verification of the current procedure.

Electricity Storage as a Matching Tool Between Variable Renewable Energy and Load

The transition to low carbon emitting solutions imposes new challenges to the power sector to accommodate a large penetration of intermittent renewables. It goes beyond the cheapest symmetrical reduction of fossil thermal generation that is being avoided. Storage is seen as the solution and also the option for batteries, but using a Discrete Fourier Transform and real hourly data it is shown that storage acts as an integral function, attenuating the “daily” and “weekly” harmonics of the charging / discharging function and leaving the “yearly” cycle as the main component to set the storage capacity needed. Export / import with neighbour systems shall be seen as a competitor with storage, but it poses mutual dependency and shared security issues. The renewable generation cost reduction in the “learning curve”, achieving a levelized cost below the variable cost of a CCGT is a milestone and it allows accepting a certain level of curtailment as an alternative to reduce the investment in storage. Batteries do not solve the long-term storage problem but today its use begins to be competitive in the “daily” cycle, replacing peaking gas plants and reinforcing the concept that the cost of storage can be seen as an equivalent thermal power plant. Better than assuming a fraction of renewable energy curtailment, it might be the development of Power-To-X solutions (hydrogen, synthetic gas, etc.) or even investing in nuclear power plants and limiting intermittent renewables penetration accordingly. Both solutions represent indirect electricity storage – fuel has a low storage cost - and it can solve the renewable surplus seasonal transfer problem, recovering synchronous generators for providing dispatchable flexibility, inertia to the system and serving as backup for periods with low renewable generation.

Sequestration of Catechol and Pentachlorophenol by Mechanochemically Treated Kaolinite

AbstractThe pollution of soils by organic contaminants, such as phenols, is a serious problem because of the high toxicity and persistence in the environment. Mechanochemical treatments (MTs) of polluted soils with minerals, such as clays and oxides, which have surfaces that exhibit catalytic properties, have been suggested to be a new useful strategy to promote both organic and inorganic pollutant degradation. Nevertheless, much still remains to be studied about the capability of clays to promote pollutant removal by means of the mechanochemical activation of the mineral surfaces. This work investigates the efficiency of the mineral kaolinite in promoting the sequestration of catechol (CAT) and pentachlorophenol (PCP) by MT. A well crystallized kaolinite (KGa-1b) was milled for prolonged times with different amounts of organic molecules so as to obtain two different clay:organic compound ratios. Prolonged grinding and a higher clay mineral:organic compound ratio were found to be more effective in promoting a stronger removal than simple contact. After 1 h of mechanochemical treatment, the PCP and CAT removal percentages were 32% and 20%, respectively. Additionally, a 7-day undisturbed incubation of the milled mixtures produced a trend for increased CAT removal (up to 40%). The interaction mechanism between kaolinite and each organic compound (i.e. CAT and PCP) after a MT was inferred by integrating information from spectroscopic, diffractometric, and chromatographic analyses. X-ray diffraction and Fourier-transform infrared data suggested a strong interaction between CAT and KGa-1b. This interaction mechanism likely occurs through the formation of an inner-sphere complex by H-bonding between the organic molecules and the oxygens of the kaolinite tetrahedral sheet. On the other hand, a weak interaction (i.e. van der Waals type) can occur between the KGa-1b O-planes and the PCP molecules, which likely bind to the external surfaces of KGa-1b.

Fourier-transform spectroscopy and potential construction of the (2)(1)Π state in KCs.

The paper presents an empirical pointwise potential energy curve (PEC) of the (2)(1)Π state of the KCs molecule constructed by applying the Inverted Perturbation Approach routine. The experimental term values in the energy range E(v', J') ∈ [15 407; 16 579] cm(-1) involved in the fit were based on Fourier-Transform spectroscopy data obtained with 0.01 cm(-1) accuracy from the laser-induced (2)(1)Π → X(1)Σ(+) fluorescence spectra. Buffer gas Ar was used to facilitate the appearance of rotation relaxation lines in the spectra, thus enlarging the (2)(1)Π data set and allowing determination of the Λ-splitting constants. The data set included vibrational v' ∈ [0, 28] and rotational J' ∈ [7, 274] quantum numbers covering about 67% of the potential well. The present PEC reproduces the overall set of data included in the fit with a standard deviation of 0.5 cm(-1). The obtained value of the Λ-doubling constant q = + 1.8 × 10(-6) cm(-1) for J' > 50 and v' ∈ [0, 6] is in an excellent agreement with q = + 1.84 × 10(-6) cm(-1) reported in Kim, Lee, and Stolyarov [J. Mol. Spectrosc. 256, 57-67 (2009)].

Potential construction of the B(1)1 Π state in KCs based on Fourier-Transform spectroscopy data

The paper presents an empirical pointwise potential energy curve (PEC) of the extensively perturbed B(1)1Π state of the KCs molecule constructed by applying an Inverted Perturbation Approach routine. The experimental term values in the energy range E(v′,J′)∈[14071;15502]cm−1 involved in the fit were based on Fourier-Transform spectroscopy data obtained with 0.01cm−1 accuracy from laser-induced B(1)1Π→X1Σ+ fluorescence spectra in the present work (654 term values) combined with 520 term values from Birzniece et al. (2012) . The data set included vibrational v′∈[0,35] and rotational J′∈[7,233] quantum numbers covering about 85% of the potential well. The present fit reproduces the data included in the fit with a standard deviation 0.94cm−1. The empirical PEC contains an inflection at about R=4.1 Åthat reflects the avoided crossing of two Ω=1 states corresponding to B(1)1Π and c(2)3Σ+ states as predicted by ab initio calculations.

Multiscale Atmospheric Dynamics: Cross-Frequency Phase-Amplitude Coupling in the Air Temperature

Interactions between dynamics on different temporal scales of about a century long record of data of the daily mean surface air temperature from various European locations have been detected using a form of the conditional mutual information, statistically tested using the Fourier-transform and multifractal surrogate data methods. An information transfer from larger to smaller time scales has been observed as the influence of the phase of slow oscillatory phenomena with the periods around 6-11yr on the amplitudes of the variability characterized by the smaller temporal scales from a few months to 4-5yr. The overall effect of the slow oscillations on the interannual temperature variability within the range 1-2 ° C has been observed in large areas of Europe.

Fourier-transform spectroscopy of (4)1Σ+ → A 1Σ+ − b 3Π, A 1Σ+ − b 3Π → X1Σ+, and $(1)^3\Delta _1\rightarrow b{\,}^3\Pi _{0^\pm }$(1)3Δ1→bΠ±3 transitions in KCs and deperturbation treatment of A 1Σ+ and b 3Π states

High resolution Fourier-transform spectroscopy data of term values in the spin-orbit (SO) coupled first excited A 1Σ+ and b 3Π states in KCs were obtained from (4)1Σ+ → A 1Σ+ − b 3Π, A 1Σ+ − b 3Π → X1Σ+, and $(1)^3\Delta _1\rightarrow b{\,}^3\Pi _{0^\pm }$(1)3Δ1→bΠ0±3 spectra of laser-induced fluorescence (LIF). About 3000 new rovibronic term values of the A 1Σ+ and $b{\,}^3\Pi _\Omega$b3ΠΩ states were obtained with an uncertainty about 0.01 cm−1 and added to the previously obtained 3439 term values in Kruzins et al. [Phys. Rev. A 81, 042509 (2010)] and 30 term values of the $b{\,}^3\Pi _{0^+}$b3Π0+ state levels below the A 1Σ+ state in Tamanis et al. [Phys. Rev. A 82, 032506 (2010)]. The data field was extended considerably, going down to vibrational level vb = 0 and up in energy to 13 814 cm−1, as compared to previously achieved vb = 14 and E = 13 250 cm−1. Overall 6431 e-symmetry term values of 39K133Cs were included in 4 × 4 coupled-channel deperturbation analysis. The analytical Morse-Long-Range (MLR) function yielded empirical diabatic potentials for the A 1Σ+ and $b{\,}^3\Pi _{0^+}$b3Π0+ states while the morphing of the SO ab initio points [J. T. Kim et al., J. Mol. Spectrosc. 256, 57 (2009)] provided the empirical diagonal and off-diagonal SO functions. Overall 98.5% of the fitted term values were reproduced with a rms (root mean square) uncertainty of 0.004 cm−1. The reliability of the model is proved by a good agreement of predicted and measured term values of the 41K133Cs isotopologue, as well as of measured and calculated intensities of (4)1Σ+ → A 1Σ+ − b 3Π LIF progressions. Direct-potential-fit of low-lying vb levels of the $b{\,}^3\Pi _{0^-}$b3Π0− component yielded the MLR potential which represents the 204 f-symmetry experimental term values with a rms uncertainty of 0.002 cm−1. The Ω-doubling of the b 3Π0 sub-state demonstrates a pronounced vb-dependent increase.

Analysis of spatial lamellar distribution from adaptive-optics second harmonic generation corneal images

The spatial organization of stromal collagen of ex-vivo corneas has been quantified in adaptive-optics second harmonic generation (SHG) images by means of an optimized Fourier transform (FT) based analysis. At a particular depth location, adjacent lamellae often present similar orientations and run parallel to the corneal surface. However this pattern might be combined with interweaved collagen bundles leading to crosshatched structures with different orientations. The procedure here reported provides us with both principal and crosshatched angles. This is also able to automatically distinguish a random distribution from a cross-shaped one, since it uses the ratio of the axes lengths of the best-fitted ellipse of the FT data as an auxiliary parameter. The technique has successfully been applied to SHG images of healthy corneas (both stroma and Bowman’s layer) of different species and to corneas undergoing cross-linking treatment.

Investigation of motion artifacts associated with fat saturation technique in 3D flash imaging

Fast low-angle shot (FLASH) imaging is widely used in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) because it permits fast and accurate T1 measurement in vivo. Suppression of the fat signal is necessary for most FLASH applications; otherwise, fat will appear hyperintense. The fat saturation technique is one popular method to reduce fat images on clinical scanners. However, fat saturation combined with the 3D FLASH sequence in breast DCE-MRI scans results in heavy ghosting artifacts caused by heartbeat. We used simulation and experimental scans to determine the cause of these artifact-enhancement phenomena. We simulated imaging of motion in the x, y, and z directions, with and without fat saturation, to investigate the origin of artifacts. Fourier transform (FT) of the whole field of view was used in the simulation, and we assumed that the uniform phantom was static during one TR. The amplitude of each echo was considered a factor in the FT data. Images were reconstructed using FT data from different phantom positions multiplied by the amplitude factor. Phantom experiments and volunteer studies were implemented to verify the conclusion. Both phantom and volunteer results showed artifacts similar to those in simulation images. We found that FLASH sequence without fat saturation is insensitive to motion. Fat saturation radiofrequency pulses placed before each group of echoes disrupted the steady state of the signal amplitude and produced a low-pass filter effect that enhanced the motion artifacts. We conclude that the low-pass filter effect associated with the fat saturation technique is responsible for dramatically increased motion artifacts.

Infrared Reflectance Kramers-Kronig Analysis by Anchor-Window Technique

An algorithm for the Kramers-Kronig analysis of the reflectivity spectra, based on an anchor-window technique is presented. The high-frequency asymptote, required for the Kramers-Kronig analysis, is determined by minimizing differences between the Kramers Kronig-deduced optical constants of a system under investigation and known optical constants measured in a small anchor-window. The algorithm is illustrated by applying it for a reconstruction of the optical conductivity sigma(omega) of the fci-ZnMgRE quasicrystals in the spectral range of 0.01-6.5 eV from the experimental IR Fourier-transform reflectivity data and the experimental spectral ellipsometry VIS-UV data. The reliability of the suggested Kramers-Kronig analysis technique is confirmed by independent infrared spectral ellipsometry sigma(omega) measurements for fci-ZnMgRE.