Power Spectral Density Analysis Research Articles

Power spectral density and similarity analysis of COVID-19 mortality waves across countries

BackgroundDuring the COVID-19 pandemic, the Johns Hopkins University Center for Systems Science and Engineering (CSSE) established a comprehensive database detailing daily mortality rates across countries. This dataset revealed fluctuating global mortality trends attributable to COVID-19; however, the specific differences and similarities in mortality patterns between countries remain insufficiently explored. Consequently, this study employs Fourier and similarity analyses to examine these patterns within the frequency domain, thereby offering novel insights into the dynamics of COVID-19 mortality waves across different nations. MethodsWe employed the Fast Fourier transform to calculate the power spectral density (PSD) of COVID-19 mortality waves in 199 countries from January 22, 2020, to March 9, 2023. Moreover, we performed a cosine similarity analysis of these PSD patterns among all the countries. ResultsWe identified two dominant peaks in the grand averaged PSD: one at a frequency of 1.15 waves per year (i.e., one wave every 10.4 months) and another at 2.7 waves per year (i.e., one wave every 4.4 months). We also found a cosine similarity index distribution with a skewness of −0.54 and a global median of cosine similarity index of 0.84, thus revealing a remarkable similarity in the dominant peaks of the COVID-19 mortality waves. ConclusionThese findings could be helpful for planetary health if a future pandemic of a similar scale occurs so that effective confinement measures or other actions could be planned during these two identified periods.

Sleep stages and brain oscillations: An in-depth analysis using quantitative and mathematical models

This study explores the intricate relationship between sleep stages and brain oscillations, utilizing quantitative EEG analysis and mathematical models. We examine the distinct EEG characteristics of light sleep, deep sleep, and REM sleep, focusing on power spectral density (PSD), coherence, and phase synchronization analyses. The Ising model and Kuramoto model provide frameworks for understanding the synchronization dynamics and phase relationships of neuronal populations across sleep stages. Our findings highlight significant variations in oscillatory activity, coherence, and phase synchronization, offering insights into the neural mechanisms underlying sleep regulation. Additionally, we discuss the implications of these findings for sleep disorders such as insomnia, sleep apnea, and narcolepsy, demonstrating how mathematical models can predict therapeutic outcomes and guide targeted interventions. This comprehensive analysis contributes to the understanding of sleep architecture and the development of novel treatments for sleep disorders.

Power Spectral Density Analysis of Midspatial Frequency Error in the BK7 Glass Lenses Polishing Process

Power Spectral Density Analysis of Midspatial Frequency Error in the BK7 Glass Lenses Polishing Process

Investigating the nature of the 2.4 hr-period eclipsing cataclysmic variable W2 in 47 Tuc

) is a cataclysmic variable (CV) in the Galactic globular cluster 47 Tucanae. Its modulation was discovered within the CATS@BAR project. The source shows all the properties of magnetic CVs, but whether it is a polar or an intermediate polar is still a matter of debate. This paper investigates the spectral and temporal properties of the source, using all archival X-ray data from and Early Data Release, to establish whether the source falls within the category of polars or intermediate polars. We fitted archival spectra with three different models: a power law, a bremsstrahlung and an optically thin thermal plasma. We also explored the temporal properties of the source with searches for pulsations with a power spectral density analysis and a Rayleigh test ($Z_n^2$). displays a mean luminosity of $ over a 20-year span, despite lower values in a few epochs. The source is not detected in the latest observation, taken with in 2022, and we infer an X-ray luminosity $ The source spectral shape does not change over time and can be equally well fitted with each of the three models, with a best-fit photon index of 1.6 for the power law and best-fit temperatures of 10 keV for both the bremsstrahlung and the thermal plasma models. We confirm the previously detected period of 8649 s, ascribed to the binary orbital period, and found a cycle-to-cycle variability associated with this periodicity. No other significant pulsation is detected. Considering the source orbital period, luminosity, spectral characteristics, long-term evolution and strong cycle-to-cycle variability, we suggest that is a magnetic CV of the polar type.

Performance of dual‐chamber oscillating water column device under irregular incident waves using Reynolds averaged Navier–Stokes model

AbstractThe purpose of this study is to analyze the hydrodynamic performance and efficiency of a dual‐chamber oscillating water column (OWC) device. For the Joint North Sea Wave Project (JONSWAP) incident waves spectrum, a two dimensional numerical wave tank is employed with nonlinear Reynolds averaged Navier–Stokes equations model along with the standard turbulence model. The free surface elevation is measured using the volume of fluid method. The numerical simulation demonstrates the streamline and velocity vector profiles throughout an entire pressure fluctuation cycle inside the chambers of the given OWC device. Further, investigation is carried out to analyze the impact of pressure drop and air flow rate through the orifice of the dual‐chamber OWC device on the power generation. Moreover, the power spectral density analysis of the free surface elevation is provided to know the variation of the parameters in the frequency domain. These results demonstrate that the effectiveness of the dual‐chamber OWC device is more near the significant wave height m.

Initial Solidification Behavior of Spring Steel Billet Near the Mold Corner During Continuous Casting Process

Strand surface defects originate from the initial solidification of molten steel in continuous casting mold. This article investigates the initial solidification behavior during the continuous casting of 55SiCr spring steel billet using a novel mold simulator. First, the high‐frequency responding temperatures and high‐frequency heat fluxes across the mold corner and hot face are calculated by the 2D‐inverse heat conduction program mathematical model and the fast Fourier transformation, and the results indicate that the temperature variations and heat flux fluctuations around mold corner are stronger than those at mold hot face. Then, the characteristic heat fluxes around the meniscus at mold corner and hot face are analyzed, and their four corresponding signals are discussed with the help of power spectral density analysis. The average solidification factor K for shell corner is greater than shell face, which indicates that the cooling intensity of shell at corner is stronger due to the two‐dimensional heat transfer near mold corner, as demonstrated by heat transfer and dendrite structure analysis. Finally, the interrelations between the shell profile, mold oscillation, heat flux change rate, high‐frequency heat flux, temperature change rate, and high‐frequency temperature for the two cases are discussed.

Power spectral density-based resting-state EEG classification of first-episode psychosis

Historically, the analysis of stimulus-dependent time–frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with first-episode psychosis (FEP), leading to an accurate diagnosis. We explore multiple machine-learning methods, including random-forest, support vector machine, and Gaussian process classifier (GPC), to demonstrate the practicality of resting-state power spectral density (PSD) to distinguish patients of FEP from healthy controls. A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper. The GPC model outperforms the other models with a specificity of 95.78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders.

Experimental study on dynamic characteristics of shock train in an isolator with different types of incident shocks

The hypersonic mixed-compression inlet, operating in an off-design state, can be categorized into low-speed and over-speed regimes based on whether the external compression shock is incident into the internal flow channel. In this study, we investigate the movement process of the shock train within an inlet/isolator under both low-speed and over-speed conditions by generating various incident shocks using wedges installed in a direct-connected ground wind tunnel. Experimental investigations are conducted to examine the dynamic characteristics of the shock train in an isolator subjected to different types of incident shocks at an incoming Mach number of 2.7. The findings reveal that varying levels of backpressure resistance for the shock train are observed with different types of incident shocks. Through the movement trajectory of the shock train leading shock (STLS) and power spectral density analysis, it is found that unilateral incident shocks result in a more intense oscillation process for the shock train with a lower dominant oscillation frequency. The dynamic mode decomposition method identifies different oscillation structures within the unsteady shock train flow field and highlights that dominant mode energy primarily concentrates at the STLS, while its symmetry is influenced by the type of incident shock. Specifically, the symmetric bilateral incident shock tends to promote a higher degree of symmetry in the STLS structure while reducing its oscillation strength; however, when the STLS passes over the reflection point of the incident shock, the rapid upstream movement of the shock train still occurs in this situation, thereby inducing inlet unstart and compromising engine safety.

Numerical modeling and simulation of microbially induced calcite precipitation on a cement surface at the pore scale

Accurate estimation of contaminant transport in cementitious material using numerical tools plays a key role in the risk assessments of nuclear waste disposal. At the pore scale, the increase of microbial activity, such as microbially induced calcite precipitation on cementitious material, causes changes in solid surface topography, pore network geometry, and pore water chemistry, which affect contaminant transport at the core scale and beyond. Consequently, a meaningful estimation of contaminant migration in the subsurface requires a pore-scale investigation of the influence of microbial activity on transport processes. In this study, a pore-scale reactive transport model is presented to simulate the physicochemical processes resulting from microbially induced calcite precipitation on a cement surface. Numerical investigations focus on modeling the reactive transport in a two-dimensional flow-through cell. The model results are validated by experimental data showing an increase in pH and a decrease in calcium concentration due to microbially induced calcite precipitation. Our results show heterogeneous calcite precipitation under transport-limited conditions and homogeneous calcite precipitation under reaction-limited conditions, resulting in non-uniform and uniform changes in the material surface topography. Moreover, power spectral density analysis of the surface data demonstrates that microbially induced calcite precipitation affects the surface topography via both general changes over the entire frequency and local modifications in the high-frequency region. The sensitivity studies provide a comprehensive understanding of the evolution of surface topography due to the microbially induced calcite precipitation at the pore scale, thus contributing to an improved predictability of contaminant transport at the core scale and beyond.

Improving the accuracy of interferometer testing with absolute surface calibration and power spectral density analysis

A novel absolute surface calibration method for interferometer testing based on power spectral density analysis has been proposed. The method involves obtaining the loss of signal at different frequencies as a function of various rotation angles by signal analysis to choose the rotation angle for calibration. A model is developed to evaluate the calibration method by creating random surface shapes based on the Power Spectral Density. Using this algorithm, the precision of the absolute testing method during the testing process exceeds 0.28 %. Error propagation during the experimental process, such as testing optic clamping, angular errors, eccentricity, vibrations and airflow disturbances, were calibrated. Ultimately, the absolute detection test results were verified using high-precision flat mirrors. The experimental result indicates an RMS of 4.275 nm, 4.263 nm, and 4.265 nm in different rotation angle, and the calibrated result shows an RMS of 4.880 nm. The shape of surface errors in the absolute test results and calibration results is consistent.

Cross-spectrum method for acoustic source identification and visualization of airfoil noise

The identification and visualization of airfoil noise sources are critical for comprehending the interplay between flow and acoustics, and for understanding the generation and propagation of sound. In this paper, we introduce an innovative method, specifically designed to identify and visualize flow-induced noise sources associated with airfoil noise. This approach employs cross power spectral density analysis to distinctly identify flow-induced noise sources. The data for our study comes from large-eddy simulations of a NACA 0012 airfoil, characterized by a Reynolds number of 4×105, a Mach number of 0.058, and an angle of attack of 6.25∘. Different cross-spectrum formulations are examined and evaluated. The analysis includes a comparison of the cross-spectrum method's strengths and limitations relative to established data-driven approaches like dynamic mode decomposition (DMD) and spectral proper orthogonal decomposition (SPOD). We find that the cross-spectrum method provides both spectral magnitudes and phase topologies, allowing it to effectively compare sound intensities among various sources at specific frequencies, while retaining high-resolution spatiotemporal coherent flow and acoustic dynamics. One of the notable advantages of the cross-spectrum method over DMD or SPOD modes is its lesser reliance on extensive data manipulation in the form of large-size data matrix, making it a more efficient and user-friendly approach for practitioners, particularly when dealing with complex systems or high-dimensional datasets. This characteristic enhances its practicality and accessibility in the field of flow and acoustic visualization. Finally, the newly developed method is applied to three distinct flow transition scenarios to evaluate its proficiency in distinguishing acoustic generation and propagation mechanisms, depending on the specific transition case.

Extracting Atmospheric Stability Information from Dual-Doppler Radar Scans in the AWAKEN Campaign

Dual-Doppler (DD) derived wind direction (β) fields are used in a novel approach to assess atmospheric stability by way of power spectral density (PSD) and bulk statistical analyses. Data on atmospheric stability, thermodynamic profiles, and wind behavior, as acquired during the AWAKEN campaign, are examined for a measurement time period featuring two stability transitions to analyze differences in flow structure as a function of stability regime. Analysis of the β fields yielded promising results when compared to stability regime and can be applied to other cases.

Dynamic temporal neural patterns based on multichannel LFPs Identify different brain states during anesthesia in pigeons: comparison of three anesthetics.

Anesthetic-induced brain activity study is crucial in avian cognitive-, consciousness-, and sleep-related research. However, the neurobiological mechanisms underlying the generation of brain rhythms and specific connectivity of birds during anesthesia are poorly understood. Although different kinds of anesthetics can be used to induce an anesthesia state, a comparison study of these drugs focusing on the neural pattern evolution during anesthesia is lacking. Here, we recorded local field potentials(LFPs) using a multi-channel micro-electrode array inserted into the nidopallium caudolateral (NCL) of adult pigeons (Columba livia) anesthetized with chloral hydrate, pelltobarbitalum natricum or urethane. Power spectral density (PSD) and functional connectivity analyses were used to measure the dynamic temporal neural patterns in NCL during anesthesia. Neural decoding analysis was adopted to calculate the probability of the pigeon's brain state and the kind of injected anesthetic. In the NCL during anesthesia, we found elevated power activity and functional connectivity at low-frequency bands and depressed power activity and connectivity at high-frequency bands. Decoding results based on the spectral and functional connectivity features indicated that the pigeon's brain states during anesthesia and the injected anesthetics can be effectively decoded. These findings provide an important foundation for future investigations on how different anesthetics induce the generation of specific neural patterns.

Studies of indium nanostructures growth models on A3B6 layered templates

This article explores the surface architecture formation on layered A3B6 semiconductors, such as In4Se3, InSe, and InTe, focusing on kinetics of surface roughness parameters, such as RMS, skewness and kurtosis, and power spectral density (PSD) under indium deposition studied through large-scale 1 × 1 µm2 and 150 × 150 nm2 scanning tunneling microscopy (STM) images analysis. Our findings reveal common kinetics of surface parameters across different crystals, suggesting fundamental similarities in their interface kinetics. Particularly, PSD analysis elucidates the role of low-frequency spatial structures, underscoring their significance in surface roughness kinetics to some extent independent of the specific layered crystal surface structure.

Stiffness calibration of qPlus sensors at low temperature through thermal noise measurements.

Non-contact atomic force microscopy (nc-AFM) offers a unique experimental framework for topographical imaging of surfaces with atomic and/or sub-molecular resolution. The technique also permits to perform frequency shift spectroscopy to quantitatively evaluate the tip-sample interaction forces and potentials above individual atoms or molecules. The stiffness of the probe, k, is then required to perform the frequency shift-to-force conversion. However, this quantity is generally known with little precision. An accurate stiffness calibration is therefore mandatory if accurate force measurements are targeted. In nc-AFM, the probe may either be a silicon cantilever, a quartz tuning fork (QTF), or a length extensional resonator (LER). When used in ultrahigh vacuum (UHV) and at low temperature, the technique mostly employs QTFs, based on the so-called qPlus design, which actually covers different types of sensors in terms of size and design of the electrodes. They all have in common a QTF featuring a metallic tip glued at the free end of one of its prongs. In this study, we report the stiffness calibration of a particular type of qPlus sensor in UHV and at 9.8 K by means of thermal noise measurements. The stiffness calibration of such high-k sensors, featuring high quality factors (Q) as well, requires to master both the acquisition parameters and the data post-processing. Our approach relies both on numerical simulations and experimental results. A thorough analysis of the thermal noise power spectral density of the qPlus fluctuations leads to an estimated stiffness of the first flexural eigenmode of ≃2000 N/m, with a maximum uncertainty of 10%, whereas the static stiffness of the sensor without tip is expected to be ≃3300 N/m. The former value must not be considered as being representative of a generic value for any qPlus, as our study stresses the influence of the tip on the estimated stiffness and points towards the need for the individual calibration of these probes. Although the framework focuses on a particular kind of sensor, it may be adapted to any high-k, high-Q nc-AFM probe used under similar conditions, such as silicon cantilevers and LERs.

EEG measurements-based study for evaluating acoustic human perception: A pilot study

Sound quality analysis and sound design are well-known human-centered strategies to evaluate the subjective perception of noise and to design machines and environments with pleasant and comfortable acoustic signatures. The subjective acoustic perception is conventionally measured by means of sound quality metrics determined through a correlation process with jury test results. The exploitation of electroencephalogram (EEG) measurements during the jury test for the registration of the brain activity in response to the acoustic stimuli presented to the jurors can allow us to estimate the jurors’ perception directly from their physiological response. This study presents results from the application of an EEG wearable device to investigate changes in the EEG frequency domain at different acoustic stimuli. Forty-three participants were recruited, and the EEG signals were recorded using the wearable sensor. The analysis of power spectral densities (PSDs) was performed to investigate features correlated to acoustic sensation induced by audio stimuli. Statistically significant differences were found between the three audio stimuli. The results bring to the conclusion that wearable sensors could be used for EEG acquisition applied to acoustic perception evaluation.

Difference between subjective and objective cognitive decline confirmed by power spectral density

ABSTRACT Introduction The study aims to use power spectrum changes in subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI), preclinical stages of Alzheimer's disease (AD), for future biomarker studies in early AD diagnosis. Methods We recruited 23 SCD and 32 aMCI subjects and conducted comparative analysis using relative power spectral density (PSD). Automated preprocessing and statistical analysis were performed using iSync Brain® (iMediSync Inc., Republic of Korea) (https://isyncbrain.com/). Results Theta band power in the temporal region was 14.826 ± 7.2394 for the SCD group and 20.003 ± 10.1768 for the aMCI group. In the parietal region, theta band power was 13.614 ± 7.5689 for SCD and 19.894 ± 11.1387 for aMCI. Beta1 band power in the frontal region was 6.639 ± 2.2904 for SCD and 5.465 ± 1.8907 for aMCI, and in the temporal region it was 7.359 ± 2.5619 for SCD and 5.921 ± 2.1605 for aMCI. Conclusion PSD analysis of resting-state EEG predicted SCD, a preclinical stage of AD. This cross-sectional study observed electrical-physiological characteristics of preclinical AD; however, follow-up studies are needed to evaluate predictive value for future cognitive decline.

A Convolutional Neural Network Classifies Beat-to-Beat Arterial Pressure Spectrograms and Wavelet Transforms according to Age, Sex, and Metabolic State: Novel Frequencies for Cardiovascular Risk Appraisal

Hemodynamic homeostasis is under the control of multiple systems. Continuous blood pressure (BP) time series carry information relevant to the pathophysiological status of the cardiovascular system. Beside average BP, different measures of variability provide prognostic value for distinguishing between cardiovascular risk states. The latter are derived from time- and frequency- domain analysis of BP recordings and are limited by their capacity to provide cumulative risk appraisal among different risk groups. The prediabetic state is associated with cardiovascular risk which is often described to be age- and sex-specific. In this study, we sought to determine distinctive features of continuous arterial pressure (AP) time-series for risk appraisal in a pre-established prediabetic rat model. To overcome the shortcomings associated with conventional BP variability parameters, we trained a convolutional neural network (CNN) using spectrograms and scalograms generated from short-term Fourier- and Morlet wavelet- transforms of AP time series, respectively, from male and female prediabetic rats fed a mild hypercaloric diet for 12- or 24- weeks and their corresponding controls. The CNN consisted of 5 convolutional layers, separated by batch normalization, ReLU activation, and max pooling. The outcome of the fifth convolutional layer had 40% of the data dropped out and delivered to a fully-connected dense layer and then to a softmax function. The data was divided into 80% training, 10% validation, and 10% test sets. The model classified spectrograms and scalograms with test accuracy of 90%+ for the different binary and multi-class classification tasks. On backward propagation of model scores, different salient frequencies were identified as critical features for classification of AP time-series. The identified frequencies were much higher than those typically used in conventional power spectral density (PSD) analysis. Using 3-way ANOVA or principal component analysis for comparison and clustering of the different cardiovascular risk groups of age, sex, and metabolic disease, respectively, according to PSD at the identified peaks does not yield suffcient risk appraisal. This indicates that the CNN possibly captures non-linear relations which are undetectable using inherently linear methods.The results indicate sex- and age-specific patterns of cardiovascular deterioration in prediabetes. Use of artificial intelligence models for the identification of abnormal BP fluctuation patterns can provide suffcient risk appraisal needed for early intervention, particularly in at-risk patients lacking clinical signs of overt cardiovascular disease. None. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Optical variability of the blazar 3C 371: From minute to year timescales

The BL Lac object 3C 371 was observed by the Transiting Exoplanet Survey Satellite (TESS) for approximately a year, between July 2019 and July 2020, with an unmatched two-minute imaging cadence. In parallel, the Whole Earth Blazar Telescope (WEBT) Collaboration organized an extensive observing campaign, providing three years of continuous optical monitoring between 2018 and 2020. These datasets allow for a thorough investigation of the variability of the source. The goal of this study is to evaluate the optical variability of 3C 371. Taking advantage of the remarkable cadence of TESS data, we aim to characterize the intra-day variability (IDV) displayed by the source and identify its shortest variability timescale. With this estimate, constraints on the size of the emitting region and black hole mass can be calculated. Moreover, WEBT data are used to investigate long-term variability (LTV), including in terms of the spectral behavior of the source and the polarization variability. Based on the derived characteristics, we aim to extract information on the origin of the variability on different timescales. We evaluated the variability of 3C 371 by applying the variability amplitude tool, which quantifies variability of the emission. Moreover, we employed common tools, such as ANOVA (ANalysis Of VAariance) tests, wavelet and power spectral density (PSD) analyses to characterize the shortest variability timescales present in the emission and the underlying noise affecting the data. We evaluated the short- and long-term color behavior to understand to understand its spectral behavior. The polarized emission was analyzed, studying its variability and possible rotation patterns of the electric vector position angle (EVPA). Flux distributions of the IDV and LTV were also studied with the aim being to link the flux variations to turbulent and/or accretion-disk-related processes. Our ANOVA and wavelet analyses reveal several entangled variability timescales. We observe a clear increase in the variability amplitude with increasing width of the time intervals evaluated. We are also able to resolve significant variations on timescales of as little as sim 0.5 hours. The PSD analysis reveals a red-noise spectrum with a break at IDV timescales. The spectral analysis shows a mild bluer-when-brighter (BWB) trend on long timescales. On short timescales, mixed BWB, achromatic and redder-when-brighter (RWB) signatures can be observed. The polarized emission shows an interesting slow EVPA rotation during the flaring period where a simple stochastic model can be excluded as the origin with a 3sigma significance . The flux distributions show a preference for a Gaussian model for the IDV, and suggest it may be linked to turbulent processes, while the LTV is better represented by a log-normal distribution and may have a disk-related.

Imbalance of sediment transport in a meso-tidal bay: effect of tidal pumping and residual circulation

In-situ mooring systems with acoustic Doppler current profilers were installed in the western (TM01) and eastern (TM02) parts of Yeosu Bay from September 3 to October 2, 2021, to understand the controlling mechanisms of suspended sediment transport. In the bay, freshwater from the Seomjin River freely exchanges with seawater from the open sea. Over the mooring period, current flows were mainly dominated by ebb tides. Power spectral density analysis of the suspended sediment concentration (SSC) exhibited a quarter-diurnal (6.21 h) frequency at TM01 and a semi-diurnal (12.42 h) frequency at TM02. The results suggested that SSC variations in the western part were driven primarily by local sediment resuspension, while they were influenced predominantly by horizontal advection in the eastern part. Differences in SSC variation at the two stations could be due to the physical properties of the bed sediments (TM01: sandy mud containing shell fragments, TM02: mud). Such current flows and SSC variations over the tidal cycles caused an imbalance of sediment transport. At TM01, the sediment fluxes were dominantly seaward due to tidal pumping (88% of the total) and the discharge of suspended sediments in the surface layer by residual circulation. At TM02, landward sediment fluxes were driven primarily by the residual current (73% of the total). Although the suspended sediment fluxes tended to be compensated and balanced mutually by circulation over the entire period, the suspended sediment fluxes at TM02 were approximately twice higher than those at TM01. This was caused by the difference in SSC asymmetry between flood and ebb at the two stations. The mechanisms controlling the transport of suspended sediment could vary spatially, and the relative contribution of tidal pumping and residual circulation could result in an imbalance of sediment transport.