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K-means clustering analysis is applied to frequency-domain thermoreflectance (FDTR) hyperspectral image data to rapidly screen the spatial distribution of thermophysical properties at material interfaces. Performing FDTR while raster scanning a sample consisting of 8.6 μ m of doped-silicon (Si) bonded to a doped-Si substrate identifies spatial variation in the subsurface bond quality. Routine thermal analysis at select pixels quantifies this variation in bond quality and allows assignment of bonded, partially bonded, and unbonded regions. Performing this same routine thermal analysis across the entire map, however, becomes too computationally demanding for rapid screening of bond quality. To address this, K-means clustering was used to reduce the dimensionality of the dataset from more than 20 000 pixel spectra to just K = 3 component spectra. The three component spectra were then used to express every pixel in the image through a least-squares minimized linear combination providing continuous interpolation between the components across spatially varying features, e.g., bonded to unbonded transition regions. Fitting the component spectra to the thermal model, thermal properties for each K cluster are extracted and then distributed according to the weighting established by the regressed linear combination. Thermophysical property maps are then constructed and capture significant variation in bond quality over 25 μ m length scales. The use of K-means clustering to achieve these thermal property maps results in a 74-fold speed improvement over explicit fitting of every pixel. [ABSTRACT FROM AUTHOR]

We study the electrothermal actuation of nanomechanical motion using a combination of numerical simulations and analytical solutions. The nanoelectrothermal actuator structure is a u-shaped gold nanoresistor that is patterned on the anchor of a doubly clamped nanomechanical beam or a microcantilever resonator. This design has been used in recent experiments successfully. In our finite-element analysis (FEA) based model, our input is an ac current; we first calculate the temperature oscillations due to Joule heating using Ohm's law and the heat equation; we then determine the thermally induced bending moment and the displacement profile of the beam by coupling the temperature field to Euler–Bernoulli beam theory with tension. Our model efficiently combines transient and frequency-domain analyses: we compute the temperature field using a transient approach and then impose this temperature field as a harmonic perturbation for determining the mechanical response in the frequency domain. This unique modeling method offers lower computational complexity and improved accuracy and is faster than a fully transient FEA approach. Our dynamical model computes the temperature and displacement fields in the time domain over a broad range of actuation frequencies and amplitudes. We validate the numerical results by directly comparing them with experimentally measured displacement amplitudes of nano-electro-mechanical system beams around their eigenmodes in vacuum. Our model predicts a thermal time constant of 1.9 ns in vacuum for our particular structures, indicating that electrothermal actuation is efficient up to ∼ 80 MHz. We also investigate the thermal response of the actuator when immersed in a variety of fluids. [ABSTRACT FROM AUTHOR]

A reduced-order homogenization framework is proposed, providing a macro-scale-enriched continuum model for locally resonant acoustic metamaterials operating in the subwavelength regime, for both time and frequency domain analyses. The homogenized continuum has a non-standard constitutive model, capturing a metamaterial behaviour such as negative effective bulk modulus, negative effective density and Willis coupling. A suitable reduced space is constructed based on the unit cell response in a steady-state regime and the local resonance regime. A frequency domain numerical example demonstrates the efficiency and suitability of the proposed framework. This article is part of the theme issue 'Current developments in elastic and acoustic metamaterials science (Part 2)'. [ABSTRACT FROM AUTHOR]

The frequency domain analysis is considered in the stationary INAR(1) process under a general innovation. The Whittle likelihood from the demeaned data is shown to be not identifiable with respect to the innovation mean and variance. Using an identifiable version of the Whittle likelihood, the strong consistency and asymptotic normality of the Whittle estimator are established. Furthermore, the Wald-type test about the equidispersion is proposed on the basis of the estimators for the innovation mean and variance. [ABSTRACT FROM AUTHOR]

Using an asymptotic method, this article deals with flexural-gravity wave scattering with undulating bottom topography, including the effect of uniform currents. The interest in this problem lies in developing second-order solutions using the Fourier transform, which minimises the error gap between first and second-order solutions. The present method allows the physical processes involved in the sea-bed topography, uniform current, plate-covered surface, and wave interaction to be studied. Specifically, we observe Bragg resonance between the flexural-gravity waves and the bottom ripples, which are associated with the reflection of incident wave energy. We examine the effects of wave current and emphasise how crucial the asymptotic expansion method is to the emergence of the current response. We demonstrate that bottom topography dominates the effects of Bragg resonance for depth Froude numbers valued at 0.8 or less. Further, most reflected wave components have their frequencies shifted by the current, and wave action conservation causes reflected wave energy to be enhanced for following currents. Using the Joint North Sea Wave Observation Project spectrum and the discrete Fourier transform, the theory derived in the frequency domain is shown in the time domain to analyse wave propagation through the whole system. • A mathematical model to study flexural-gravity wave scattering with undulating bottom in the presence of uniform current. • The problem is studied using an asymptotic approach under linear water wave theory. • Develop the second-order solutions using the Fourier transform technique. • Identify the existence of Bragg resonance between the flexural-gravity waves and the bottom ripples. • The frequency domain analysis is illustrated in the time domain using JONSWAP. [ABSTRACT FROM AUTHOR]

Calcium imaging, especially two-photon imaging, has become essential in neuroscience for studying neuronal and astrocytic activity under in vivo and in vitro conditions. Current advances in the development of calcium sensors as well as imaging hardware enable high-frequency measurements of calcium signals in hundreds of cells simultaneously. The analysis of these large datasets requires special tools and usually a certain level of programming experience. Despite advancements in calcium imaging analysis software development, significant gaps remain, particularly for data acquired at a high sampling rate that would allow for the spectral analysis of calcium signals. The FluoAnalysis MATLAB toolbox addresses these gaps by offering a comprehensive solution for analyzing simultaneously measured calcium imaging and electrophysiological data. It features both GUI-based and command-line approaches, emphasizing frequency domain analysis to reveal network-level oscillatory signals linked to single-cell activity. In addition, the toolbox puts special emphasis on differentiating between astrocytes and neurons, revealing the interactions between the network activity of the two major cell types of the brain. It facilitates a streamlined workflow for data loading, ROI identification, cell classification, fluorescence intensity calculation, spectral analysis, and report generation, supporting both manual and automated high-throughput analysis. This versatile platform enables the comprehensive analysis of large imaging datasets. In conclusion, the FluoAnalysis MATLAB toolbox provides a robust and versatile platform for the integrated analysis of calcium imaging and electrophysiological data, supporting diverse neuroscience research applications. [ABSTRACT FROM AUTHOR]

Corrosion of steel rebar in reinforced concrete (RC) constructions is an issue of great concern toward ensuring the integrity of civil structures, and thus, detection of the on-set of corrosion process, if any, will tremendously help in taking timely and effective measures. To address this issue, the current work aims to evaluate the potential of using different nonlinear ultrasonic (NLU) methods to detect corrosion through analysis of the signal characteristics from both time and frequency domain. Three NLU parameters (from time and frequency domain response) are studied by using an indirect type of measurement obtained from RC specimens undergoing accelerated corrosion process. Signal subtraction method (SSM)-based time domain nonlinear parameter and sideband peak count (SPC)-based frequency domain nonlinear parameter are found to be effective in detecting the specific damage stages during the corrosion process. There exists a significant shift in the cumulative nonlinear parameter curve arrived based on SSM when the mass loss is around 14 g, corresponding to the time at which the first surface crack of about 0.2 mm width appeared. Cumulative nonlinear parameter curve arrived based on SPC, shows a drastic shift when the mass loss is only 2 g, i.e., at the time when microcrack initiates inside concrete. To provide a complete monitoring solution, the complementary abilities of both time and frequency domain NLUs are used for the first time to formulate a damage index that can effectively monitor the progression of damage caused by corrosion from the beginning of the process until the point of failure. [ABSTRACT FROM AUTHOR]

It is widely acknowledged that fluidelastic instability (FEI), among other mechanisms, is of the greatest concern in the flow-induced vibration (FIV) of tube bundles in steam generators and heat exchangers. A range of theoretical models have been developed for FEI analysis, and, in addition to the earliest semi-empirical Connors' model, the unsteady model, the quasi-steady model and the semi-analytical model are believed to be three advanced models predominant in the literature. The unsteady and the quasi-static models share the merits of having explicit fluid force expressions and ease of being implemented but require more experimental inputs, whereas the semi-analytical model requires fewer parameters due to its analytical nature but is hard, if not prohibitive, to derive explicit fluid force expressions. Since the fluid force formulations set in the core of development of FEI models, the understanding and in particular the implementation of the semi-analytical model has been impaired by the nonexistence of explicit fluid force expression. This issue becomes more profound in time-domain analysis whereby the simple harmonic assumption is discarded. Here we report a new semi-analytical time-domain (SATD) FEI model with explicit fluid force expressions. The new model allows a consistent frequency-domain stability analysis and more importantly a truly time-domain response analysis. The theory was validated by calculating linear stability thresholds of two typical tube array patterns and comparing against reported experimental data. We then present a nonlinear time-domain analysis of a single loosely-supported tube with piece-wise linear stiffness. The nonlinear and nonsmooth dynamics was probed in details by utilizing various techniques, playing an emphasis on characterizing and distinguishing the chaotic vibration. We found that the system follows a quasi-periodic route to chaos. Such an in-depth study of the nonlinear dynamics of tubes in crossflow has never been reported in the context of SATD model. Our results enrich the theory and provide a different approach for linear and nonlinear dynamics of tube bundles, which are essential for the subsequent fretting wear analysis. • A new formulation on semi-analytical time-domain model for fluidelastic instability of tube bundles. • The formulation has explicit fluid force expressions. • Linear frequency- and time-domain analysis are validated against available experimental and theoretical results. • Nonlinear dynamics behavior of a loosely-supported tube is probed based on the new formulation by using various techniques. • The route to chaos of the system is found through quasi-periodic to chaos. [ABSTRACT FROM AUTHOR]

Modulating characteristics of transmissive and reflective Fabry–Perot type multi-nanolayer conductor–dielectric electro-optical modulators (EOMs) for chip-to-chip free space optical interconnection are analyzed via wavelength-scale electromagnetic modelling. For electromagnetic analysis the frequency-domain method of single expression is used. The considered EOM structures consist of an electro-optical spacer of LiNbO3 covered by two thin ITO conducting nano-layers, surrounded by Si/SiO2 distributed Bragg reflectors (DBRs). In the case of transmissive EOM the DBRs are symmetric regarding the spacer, while in the case of reflective EOM they are asymmetric to provide high reflectance for the structure. From four possible types of DBRs the suitable structure has been chosen with layers of higher permittivity adjoining to the ITO nano-layers. ITO nano-layers serving as electric contacts for supplying modulating electrical signal to the electro-optical spacer are parts of the multi-nanolayer structures and are included in the electromagnetic models. An incident radiation from an external laser diode at 1.55 μm wavelength is considered. The optimal configurations of the EOM structures providing a high peak in the transmittance for the transmissive EOM and a narrow dip in the reflectance for the reflective EOM are proposed. Efficiency of optical wave intensity modulation is analyzed by means of influence of electro-optical spacer's permittivity change on the transmittance and the reflectance of the EOM structures. [ABSTRACT FROM AUTHOR]

This paper presents a pioneering cascade estimator, CRESO, which merges reduced-order and full-order extended state observers (ESO) in a novel manner. CRESO is designed to navigate the trade-off between robustness, estimation accuracy, and noise amplification inherent in active disturbance rejection control (ADRC) schemes. An analysis in the frequency domain substantiates CRESO's performance and robustness capabilities compared to those of single-level ESO and cascade ESO (CESO). These features are quantified using practical metrics, such as stability margins, sensitivity bandwidth, and estimation error at low frequencies. Additionally, the discussion encompasses the impact of selecting bandwidths for the cascade levels of CRESO on noise suppression. Experimental validation on a synchronous buck converter demonstrates the effectiveness of CRESO-based ADRC against control gain uncertainties, frequency-varying external disturbances, and sensor noise. The results highlight the advantages of the proposed approach over ADRC strategies employing singular ESO, two-level CESO, and two independent ESOs, as evidenced by several quality indices derived from the tracking errors and control signals. • Novel cascade estimator for ADRC, CRESO, which merges reduced-order and full-order ESO. • New alternative to balance robustness, estimation accuracy and noise levels in ADRC. • Improved robustness and disturbance attenuation compared to cascade ESO. • A frequency-domain analysis substantiates the performance and robustness of CRESO. • Experiments confirm the benefits of the proposed approach over existing ADRC solutions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Civil Engineering & Architecture is the property of Journal of Beijing University of Civil Engineering & Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Strong earthquake disasters can cause noticeable damage correlations among regional buildings with similar mechanical properties, termed structure‐to‐structure seismic damage correlation, which has a pronounced impact on the regional seismic risk assessment and therefore needs to be properly quantified. This study first introduces a time‐domain analytical and equivalent frequency‐domain analysis based on random vibration theory for calculating structure‐to‐structure seismic damage correlation coefficients. Subsequently, by employing the spatially consistent white noise excitation and the spatially varying white noise excitation with the Luco–Wong coherence function, the structural filtering effect and the ground motion spatial correlation effect are progressively incorporated, and an analytical interstructural damage correlation model incorporating structural dynamic properties and spatial distance is derived. Comparations with Monte Carlo simulations and existing empirical models demonstrate that the proposed analytical model possesses a clear physical basis and high reliability. Finally, a case study was conducted on a district having 29,461 buildings in Shanghai, China to illustrate the influence of interstructural damage correlation on the regional seismic risk. Results show that disregarding the interstructural seismic damage correlation can lead to underestimation of overall loss uncertainty. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhejiang University: Science A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cavitation phenomena are accompanied by harsh cavitation noise. Studying cavitation noise is an effective method to monitor cavitation conditions. In order to study the characteristics of cavitation noise, a Venturi test bench experiment platform is built, the cavitation flow field and cavitation noise of Venturi test bench are simulated. The frequency domain analysis of cavitation noise is carried out, and the relationship between cavitation region, flow rate and sound pressure level of cavitation noise is verified by comparison between simulation and experiment. Because the cavitation noise is easily polluted by ambient noise, wavelet transform is used to analyze the cavitation noise and background noise, and the characteristics of cavitation noise are studied by comparing the difference between them. [ABSTRACT FROM AUTHOR]

The analysis of soil–structure interaction (SSI) problems has been established successfully in recent decades. In particular, the solution in the frequency domain provides an exact and efficient method for computing the response of the coupled system. Despite this, the state of practice as a first attempt to incentivize time domain analyses compatible with standard finite element packages introduces the so‐called dimensionless flexible‐base frequency. This frequency, which depends solely on the structure‐to‐soil‐period ratio, allows transforming the frequency domain analyses into time domain analyses using frequency‐independent soil impedance values. However, if such frequency exists for the combined system, it must depend on several physical variables. In this work, we propose a supervised approach to obtain the flexible‐base dimensionless frequency at which the frequency‐independent soil impedance should be used. The analysis is carried out using five dimensionless parameters, and the importance of each one to the estimation of the dimensionless flexible‐base frequency is investigated. We use an inverse problem based on ensemble Kalman inversion (EnKI) to obtain the optimal frequency of the interaction. The data obtained are then employed in a machine‐learning framework to map a set of dimensionless parameters to such a frequency. The generated mapping is finally verified, and a significant improvement in time‐domain simulations is shown compared to the state of practice. [ABSTRACT FROM AUTHOR]

DC circuit breakers (DCCBs) with high breaking capacity and low cost are necessary for quick fault clearance in DC networks. The assembly DC circuit breakers (ADCCBs) have a main breaking section (MBS) and a sub-breaking sections (SBS) for each line, which greatly reduce the cost. But in conventional operation, it bears high voltage for a long time when there is a main switch grounding process in any line fault action. To address this problem, a multiport assembly circuit breaker based on current injection (CI-MPACB) is proposed, which is able to generate a resonant current with increasing amplitude by controlling the duty cycle of Integrated Gate-Commutated Thyristors (IGCTs). Then the resonant current is injected into the SBS to generate current zero crossing and arc extinction. A complex frequency domain circuit analysis is performed on the MBS to describe the action logic as well as the commutation characteristics. In addition, the parameters of each component of the MBS are subject to multiple constraints and reasonable design to ensure the fault current could be cut off quickly and reliably. The cost of existing design is greatly reduced due to the design idea of resonant current injection device parameter selection. Finally, a PSCAD/EMTDC simulation confirms the opening viability of CI-MPACB and the accuracy of the parameter design. The test results show that the designed CI-MPACB can cut off DC fault lines. [ABSTRACT FROM AUTHOR]

Used extensively in structural dynamics analysis, the energy method is advantageous for transforming boundary value problems of differential equations into variational extremum problems. Recent applications include analyzing the wave and vibration characteristics of track structures. However, traditional energy methods for dynamic modeling and analysis of ballastless track structures require obtaining the global stiffness matrix and mass matrix of the entire coupled system, which decreases computational efficiency. The EM-CMS algorithm for frequency domain dynamic response analysis of ballastless track structures has been proposed to address this issue. The core of EM-CMS is the development of model reduction strategies within the framework of the energy method to reduce matrix dimensions and thereby improve computational efficiency. Specifically, the steel spring floating slab track is the focus of this research. Utilizing the energy functional variational method, the modal properties of the rail and floating slab structures are obtained, and truncation is performed. A reduced-order model for the steel spring floating slab track is established by considering the boundary conditions between the rail-floating slab and the floating slab foundation (connected through fastener springs and steel springs, respectively, and considering the springs' elastic potential energy). Comparatively, the computational efficiency of EM-CMS is approximately ten times greater than that of the energy method. The method's accuracy is also carefully validated against finite element simulations. [ABSTRACT FROM AUTHOR]

This paper presents a deep-learning-based method to detect recreational vessels. The method takes advantage of existing underwater acoustic measurements from an Estuarine Soundscape Observatory Network based in the estuaries of South Carolina (SC), USA. The detection method is a two-step searching method, called Deep Scanning (DS), which includes a time-domain energy analysis and a frequency-domain spectrum analysis. In the time domain, acoustic signals with higher energy, measured by sound pressure level (SPL), are labeled for the potential existence of moving vessels. In the frequency domain, the labeled acoustic signals are examined against a predefined training dataset using a neural network. This research builds training data using diverse vessel sound features obtained from real measurements, with a duration between 5.0 seconds and 7.5 seconds and a frequency between 800 Hz to 10,000 Hz. The proposed method was then evaluated using all acoustic data in the years 2017, 2018, and 2021, respectively; a total of approximately 171,262 2-minute.wav files at three deployed locations in May River, SC. The DS detections were compared to human-observed detections for each audio file and results showed the method was able to classify the existence of vessels, with an average accuracy of around 99.0%. [ABSTRACT FROM AUTHOR]

The ubiquitous adoption of Android devices has unfortunately brought a surge in malware threats, compromising user data, privacy concerns, and financial and device integrity, to name a few. To combat this, numerous efforts have explored automated botnet detection mechanisms, with anomaly-based approaches leveraging machine learning (ML) gaining attraction due to their signature-agnostic nature. However, the problem lies in devising accurate ML models which capture the ever evolving landscape of malwares by effectively leveraging all the possible features from Android application packages (APKs).This paper delved into this domain by proposing, implementing, and evaluating an image-based Android malware detection (AMD) framework that harnessed the power of feature hybridization. The core idea of this framework was the conversion of text-based data extracted from Android APKs into grayscale images. The novelty aspect of this work lied in the unique image feature extraction strategies and their subsequent hybridization to achieve accurate malware classification using ML models. More specifically, four distinct feature extraction methodologies, namely, Texture and histogram of oriented gradients (HOG) from spatial domain, and discrete wavelet transform (DWT) and Gabor from the frequency domain were employed to hybridize the features for improved malware identification. To this end, three image-based datasets, namely, Dex, Manifest, and Composite, derived from the information security centre of excellence (ISCX) Android Malware dataset, were leveraged to evaluate the optimal data source for botnet classification. Popular ML classifiers, including naive Bayes (NB), multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were employed for the classification task. The experimental results demonstrated the efficacy of the proposed framework, achieving a peak classification accuracy of 93.03% and recall of 97.1% for the RF classifier using the Manifest dataset and a combination of Texture and HOG features. These findings validate the proof-of-concept and provide valuable insights for researchers exploring ML/deep learning (DL) approaches in the domain of AMD. [ABSTRACT FROM AUTHOR]

Traditional predictive models, often used in simpler settings, face issues like high latency and computational demands, especially in complex real-world environments. Recent progress in deep learning has advanced spatiotemporal prediction research, yet challenges persist in general scenarios: (ⅰ) Latency and computational load of models; (ⅱ) dynamic nature of real-world environments; (ⅲ) complex motion and monitoring scenes. To overcome these challenges, we introduced a novel spatiotemporal prediction framework. It replaced high-latency recurrent models with fully convolutional ones, improving inference speed. Furthermore, it addressed the dynamic nature of environments with multilevel frequency domain encoders and decoders, facilitating spatial and temporal learning. For complex monitoring scenarios, a large receptive field token mixer spatial-frequency attention units (SAU) and time attention units (TAU) ensured temporal and spatial continuity. This framework outperformed current methods in accuracy and speed on public datasets, showing promising practical applications beyond electricity monitoring. [ABSTRACT FROM AUTHOR]

This paper focuses on the use of vibration measurements for the purpose of cost-effective performance evaluation for the safety management and maintenance of Japan's social infrastructure like bridges. Since modal properties are often used to diagnose damage of structures by analysing their changes, various modal identification methods have been developed in the past few decades. Among these, the FDD method has still attractive attention because of its simplicity and practicality. It is also highly applicable to simultaneous observation at multiple points and even complex modes can be identified instantly. On the other hand, the applicability of this method to impact tests applied to evaluate the condition of structures has not been sufficiently discussed to date. In this study, we will clarify the applicability to impact tests by reconstructing the theoretical background of the FDD method. Furthermore, we will show from theory that when there is a correlation between inputs, higher-order singular values, which should be noted when applied to impact tests, will be affected. The conclusions obtained from the reconstruction of the theoretical background will be verified based on numerical experiments and actual observation records. [ABSTRACT FROM AUTHOR]

The non‐relativistic motion of media induces very weak effects on electromagnetic fields. For this reason, it is extremely difficult to compute such effects with traditional techniques. To overcome this difficulty, a novel approach is introduced in this work. It is based on the Born approximation of the solution of electromagnetic problems involving media in motion. The formulation of these problems makes use of equivalent field sources and is specifically tailored to find the effects of motion on the electromagnetic field. The new methodology exploits traditional simulators able to deal with media at rest. The motion of the materials has to be managed by specific programs performing simple algebraic calculations. The simplest of the methods proposed does not present any additional complexity with respect to more traditional approaches to the same problems. All methods deal with time‐harmonic electromagnetic fields and, for this reason, they can manage materials in motion with stationary boundaries. A complete set of simulations for cylinders moving in the axial direction show that the new methods outperform traditional numerical approaches and, in some significant cases, traditional approaches based on semi‐analytical techniques. The good features of the new methodology are shown to hold true for a range of velocity values spanning 11 decades; such a range covers most of the applications of practical interest. [ABSTRACT FROM AUTHOR]

Based on the reconstructed MODIS data and ECMWF reanalysis data from 2003 to 2021, spatial correlations between chlorophyll a (Chl a) and sea surface temperature (SST), photosynthetically available radiation (PAR), aerosol optical thickness (AOT), and wind speed (WS) in the Bohai Sea were analyzed from the perspective of time domain and frequency domain. Results indicate that the frequency-domain analysis was more conducive to revealing the correlations between Chl a and environmental factors. The spatial pattern of time-domain correlations was similar to the isobaths of the Bohai Sea, which was positive in shallow waters and negative in deep waters for SST, PAR, and AOT, and was reversed for WS. Frequency-domain correlations were obtained by performing Fourier Transform and were higher than correlations in time domain. The spatial distributions indicated that the effects of SST and PAR on Chl a were greater than AOT and WS in the Bohai Sea. Additionally, cross-spectrum analysis was applied to explore the response relationships. A depth-dependent pattern was shown in correlations and time lags, indicating that the influential mechanism of environmental factors on Chl-a concentration is related to seawater depth. [ABSTRACT FROM AUTHOR]

Time–frequency analysis is one of the effective methods for seismic thin‐layer detection. Conventional time–frequency analysis technology for seismic thin‐layer detection is interfered by the energy of adjacent frequency signals, and there is information redundancy in the frequency‐domain analysis. Therefore, an S transform with improved window factor, which is based on the constrained non‐negative matrix factorization, is constructed to realize seismic thin‐layer detection. First, the seismic data is processed by the S transform of the improved window factor, and then we can obtain the frequency‐domain information with strong time–frequency focus by changing the adjustment factor and attenuation factor in the window function. Furthermore, the key frequency of the seismic data spectrum, which can also be called the key frequency characteristic factor, can be calculated by the non‐negative matrix factorization algorithm. Fortunately, the overthrust model shows a good correspondence between the key frequency characteristic factor and the thin‐layer interface. The field data example shows that this approach provides a new approach for thin‐layer detection. [ABSTRACT FROM AUTHOR]

This paper introduces a sophisticated approach for identifying and categorizing broken rotor bars in direct torque-controlled (DTC) induction motors. DTC is implemented in industrial drive systems as a suitable control method to preserve torque control performance, which sometimes shows its impact on fault-representing frequencies. This is because of the DTC's closed-loop control nature, whichtriesto reduce speed and torque ripples by changing the voltage profile. The proposed model utilizes the modified Shapley Additive exPlanations (SHAP) technique in combination with gradient-boosting decision trees (GBDT) to detect and classify the abnormalities in BRBs at diverse (0%, 25%, 50%, 75%, and 100%) loading conditions. To prevent overfitting of the proposed model, we used the adaptive fold cross-validation (AF-CV) technique, which can dynamically adjust the number of folds during the optimization process. By employing extensive feature engineering in the original dataset and then applying Shapely Additive exPlanations(SHAP)-based feature selection, our methodology effectively identifies informative features from signals (three-phase current, three-phase voltage, torque, and speed) and motor characteristics. The gradient-boosting decision tree (GBDT) classifier, trained using the given characteristics, extracts consistent and reliable classification performance under different loading circumstances and enables precise and accurate detection and classification of broken rotor bars. The proposed approach (SHAP-Fusion GBDT with AF-CV) is a major advancement in the field of machine learning in detecting motor anomalies at varying loading conditions and proved to be an effective mechanism for preventative maintenance and preventing faults in DTC-controlled induction motors byattaining an accuracy rate of 99% for all loading conditions. [ABSTRACT FROM AUTHOR]

We performed a stability analysis of a 2D wave-plate coupling system equipped with memory viscoelastic damping. The study highlights the unique functionality of the damping mechanism, which operates indirectly and exclusively within either the wave or plate subsystem. The opposing subsystem receives dissipative signals indirectly through the coupling component. The primary objective of this study was to determine whether the indirect memory damping is sufficient to ensure the overall stability of the coupled system. To address this question, a frequency domain analysis was employed to establish explicit decay rates of the coupled system. Notably, a polynomial decay rate is observed when the memory damping is applied solely to either the plate or wave subsystem, which provides a conclusive answer to the posed question. [ABSTRACT FROM AUTHOR]

A vectorized analytical model for the cutting dynamics in the spiral bevel gear face hobbing process has been developed, which is based on machine tool kinematics and vibration vectorization. The structural modal parameters of the cutter head spindle system are obtained through experimental modal analysis with hammer impact testing. The analytical model is utilized to simulate the generation of simulated vibration acceleration signals during spiral bevel gear hobbing. A wavelet threshold denoising method is applied to process the simulated vibration signals of the spiral bevel gear face hobbing with added white noise. Signal processing methods, including short-time Fourier transform are employed for time-domain analysis, frequency-domain analysis, and time–frequency-domain analysis of measured signals and simulated signals, thereby extracting the corresponding statistical features. In addition to the results of the experimental modal analysis, the causes of chatter in spiral bevel gear hobbing are discussed in detail, revealing that the main factor is cutter head vibration in the Y direction of the Hunan ZDCY CNC EQUIPMENT YKA2260 machine tool used in this research. The error in the time-domain characteristic parameters between simulated signals and measured vibration acceleration signals is within 15%, with a difference of 3.5% in spectral peak values. The predicted tooth surface morphology from simulation matches the actual morphology on the workpiece, comprehensively validating the reliability of the cutting dynamics model for the spiral bevel gear face hobbing process. Another conclusion drawn from numerical simulation experiments is that the amount of tooth surface waviness of the spiral bevel gears is the ratio of tool chatter frequency to cutting fundamental frequency. [ABSTRACT FROM AUTHOR]

A good planting bed is a prerequisite for improving planting quality, while complex ground excitation often leads to machine bouncing and operation vibration, which then affects the operation effect. In order to improve the quality of rotary tillage operations, it is necessary to study the effects of various vibration excitations on the unit during tractor rotary tillage operations and analyze the vibration interaction relationship among the tractor, the three-point suspension mechanism, and the rotary tiller. For this purpose, multiple three-way acceleration sensors were installed at different positions on the rotary tiller unit of a Lexing LS1004 tractor(Lexing Agricultural Equipment Co. Ltd., Qingdao, China) to collect vibration data at different operating speeds and conduct vibration characteristic analysis between different components. The test results showed that when the unit moved forward at 2.1 km/h, 3.6 km/h, and 4.5 km/h, respectively, the vibration acceleration of the tractor, the three-point suspension mechanism, and the rotary tiller increased with the increase in speed, and there was indeed interaction between them. The vertical acceleration change during the test in the three-point suspension mechanism was the most significant (5.914 m/s2) and was related to the increase in the speed of the vehicle and the vibration transfer of the rotary tiller. Meanwhile, the vertical vibration acceleration of the tractor's symmetrical structure was not similar, suggesting the existence of structural assembly problems. From the perspective of frequency domain analysis, the resonant frequency at the cab of the tractor was reduced in a vertical vibration environment, with relatively low frequencies (0~80 Hz) and small magnitudes, which might be beneficial to the driver's health. The rotary tillage group resonated around 350 Hz, and this characteristic can be used to appropriately increase the vibration of the rotary tiller to reduce resistance. The tractor cab resonated around 280 Hz, which must be avoided during field operations to ensure driver health and reduce machine wear. The research results can provide a reference for reducing vibration and resistance during tractor rotary tillage operations, as well as optimizing and improving the structure of rotary tillers and tractors. [ABSTRACT FROM AUTHOR]

Carbon fiber-reinforced polymer (CFRP) components were extensively used and current studies mainly refer to CFRP laminates. The dynamic performance of the complicated CFRP antenna beams is yet to be explored. Therefore, a sensor layout based on fiber Bragg gratings (FBGs) in series was designed to measure the dynamic response of the CFRP antenna beam, and various vibration tests (sweep frequency test, simulated long-life vibration test, shock vibration test, functional vibration test, and constant frequency vibration test) were conducted. The time and frequency domain analysis on FBG sensing signals was performed to check the vibration performance and assess the health condition of this novel CFRP structure. The results indicate that strain values reach a maximum of only 300 µε under different test conditions. The antenna beam exhibited clear vibration patterns, with the first four intrinsic frequencies identified at 44, 94.87, 107.1, and 193.45 Hz. It shows that strain distribution and vibration modes of the antenna beam can be characterized from the sensing data, and the dynamic feature can be much more accurately assessed. The FBG sensors attached on the surface of CFRP antenna beam can accurately and stably measure the dynamic response, which validates that the interfaces between optical fiber sensing elements and CFRP material have excellent interfacial bonding characteristics. The novel CFRP antenna beam exhibits the excellent dynamic performance and stability, offering the replacement of traditional steel antenna beams. The study can finally instruct the development of self-sensing CFRP antenna beams assembled with FBGs in series. [ABSTRACT FROM AUTHOR]

Blind tees, as important junctions, are widely used in offshore oil and gas transportation systems to improve mixing flow conditions and measurement accuracies in curved pipes. Despite the significance of blind tees, their unsteady flow characteristics and mixing mechanisms in turbulent flow regimes are not clearly established. Therefore, in this study, Unsteady Reynolds-Averaged Navier–Stokes (URANS) simulations, coupled with Explicit Algebraic Reynolds Stress Model (EARSM), are employed to explore the complex turbulent flow characteristics within blind-tee pipes. Firstly, the statistical flow features are investigated based on the time-averaged results, and the swirl dissipation analysis reveals an intense dissipative process occurring within blind tees, surpassing conventional elbows in swirling intensity. Then, the instantaneous flow characteristics are investigated through time and frequency domain analysis, uncovering the oscillatory patterns and elucidating the mechanisms behind unsteady secondary flow motions. In a 2D-length blind tee, a nondimensional dominant frequency of oscillation (Stbt = 0.0361) is identified, highlighting the significant correlation between dominant frequencies inside and downstream of the plugged section, which emphasizes the critical role of the plugged structure in these unsteady motions. Finally, a power spectra analysis is conducted to explore the influence of blind-tee structures, indicating that the blind-tee length of lbt = 2D enhances the flow-mixing conditions by amplifying the oscillation intensities of secondary flow motions. [ABSTRACT FROM AUTHOR]

Abstract The non‐relativistic motion of media induces very weak effects on electromagnetic fields. For this reason, it is extremely difficult to compute such effects with traditional techniques. To overcome this difficulty, a novel approach is introduced in this work. It is based on the Born approximation of the solution of electromagnetic problems involving media in motion. The formulation of these problems makes use of equivalent field sources and is specifically tailored to find the effects of motion on the electromagnetic field. The new methodology exploits traditional simulators able to deal with media at rest. The motion of the materials has to be managed by specific programs performing simple algebraic calculations. The simplest of the methods proposed does not present any additional complexity with respect to more traditional approaches to the same problems. All methods deal with time‐harmonic electromagnetic fields and, for this reason, they can manage materials in motion with stationary boundaries. A complete set of simulations for cylinders moving in the axial direction show that the new methods outperform traditional numerical approaches and, in some significant cases, traditional approaches based on semi‐analytical techniques. The good features of the new methodology are shown to hold true for a range of velocity values spanning 11 decades; such a range covers most of the applications of practical interest.

Abstract Power outages can cause harm to both society and individual health. Among the crucial elements of power systems, power transformers play a vital role, and their failures can lead to incidents and disruptions. According to statistics, almost 50% of the explosions and fires in the power transformer are caused by oil‐impregnated paper (OIP) bushing failures. Moreover, moisture is a crucial factor that affects the health of OIP bushings, and assessing the moisture level in OIP bushings is vital to guarantee the safety of power systems. While the frequency‐domain dielectric spectroscopy (FDS) method has been widely used to assess the moisture content of OIP bushings on‐site, it has the disadvantage of requiring a long measurement time. Therefore, a novel technique was proposed in this study that involves measuring the OIP bushing dissipation factor (tanδ) at 1 Hz to estimate the moisture content in a significantly shorter time. Five samples of OIP with various moisture levels have been selected, and an equation based on the OIPs' value of tanδ at 1 Hz was proposed to assess the moisture level of OIP bushings. Furthermore, the validity of the proposed method was successfully verified, proving its precision and time efficiency.

From a mathematical viewpoint, the frequency domain analysis of vessel motion responses due to wave actions is based on integration of system dynamics idealized in terms of response amplitude operators for six degrees-of-freedom (DOFs) rigid body motions and an input wave spectrum in order to obtain the response spectrum. Various quantities of interest can be deduced from the response spectrum, which are then used for deriving response-based operational limits for marine operations, also including extreme value and fatigue analysis. The variation of such quantities, owing to the uncertainties associated with the vessel system parameters, can be quantified by performing uncertainty propagation and consequent sensitivity analysis. This study emphasizes and proposes a computational-efficient way of assessing the sensitivity of the system model output with respect to the uncertainties residing in the input parameters by operating on a surrogate model representation. In this respect, the global sensitivity analysis was effectively carried out by deploying an efficient nonintrusive polynomial chaos expansion surrogate model built using a point collocation strategy. Successively, Sobol' indices were obtained from the analytical decomposition of the polynomial coefficients. The indices, eventually, are employed to quantitatively measure the effects of input uncertainties on the output 6DOF vessel root-mean-square responses. [ABSTRACT FROM AUTHOR]

Feature extraction is a crucial component in the analysis of piano music signals. This article introduced three methods for feature extraction based on frequency domain analysis, namely short-time Fourier transform (STFT), linear predictive cepstral coefficient (LPCC), and Mel-frequency cepstral coefficient (MFCC). An improvement was then made to the MFCC. The inverse MFCC (IMFCC) was combined with mid-frequency MFCC (MidMFCC). The Fisher criterion was used to select the 12-order parameters with the maximum Fisher ratio, which were combined into the F-MFCC feature for recognizing 88 single piano notes through a support vector machine. The results indicated that when compared with the STFT and LPCC, the MFCC exhibited superior performance in recognizing piano music signals, with an accuracy rate of 78.03 % and an F1 value of 85.92 %. Nevertheless, the proposed F-MFCC achieved a remarkable accuracy rate of 90.91 %, representing a substantial improvement by 12.88 % over the MFCC alone. These findings provide evidence for the effectiveness of the designed F-MFCC feature for piano music signal recognition as well as its potential application in practical music signal analysis. [ABSTRACT FROM AUTHOR]

This paper uses an improved switching table-based direct torque control space vector selection technique with a nine-level hysteresis band to reduce torque ripple in a PMBLDC drive. The commutation torque ripple is mitigated by different rates of correction of torque depending upon the error quantum. Multi-rates of correction are applied using three sets of lookup tables corresponding to low, medium and high torque errors. In general, the existing state of the art incorporates an observer-based controller that optimizes complex objective functions. This increases computational cost and complexity. The proposed method is based on modified switching tables. This decreases the computational cost and improves the dynamic response. Frequency-domain analysis of torque response establishes the minimization of high-frequency components. The experimental validation is obtained on 1 kw BLDC motor using DSPACE 1104 real-time controller. [ABSTRACT FROM AUTHOR]

Cerebral blood flow (CBF) autoregulation is the physiologic process whereby blood supply to the brain is kept constant over a range of cerebral perfusion pressures ensuring a constant supply of metabolic substrate. Clinical methods for monitoring CBF autoregulation were first developed for neurocritically ill patients and have been extended to surgical patients. These methods are based on measuring the relationship between cerebral perfusion pressure and surrogates of CBF or cerebral blood volume (CBV) at low frequencies (<0.05 Hz) of autoregulation using time or frequency domain analyses. Initially intracranial pressure monitoring or transcranial Doppler assessment of CBF velocity was utilised relative to changes in cerebral perfusion pressure or mean arterial pressure. A more clinically practical approach utilising filtered signals from near infrared spectroscopy monitors as an estimate of CBF has been validated. In contrast to the traditional teaching that 50 mm Hg is the autoregulation threshold, these investigations have found wide interindividual variability of the lower limit of autoregulation ranging from 40 to 90 mm Hg in adults and 20–55 mm Hg in children. Observational data have linked impaired CBF autoregulation metrics to adverse outcomes in patients with traumatic brain injury, ischaemic stroke, subarachnoid haemorrhage, intracerebral haemorrhage, and in surgical patients. CBF autoregulation monitoring has been described in both cardiac and noncardiac surgery. Data from a single-centre randomised study in adults found that targeting arterial pressure during cardiopulmonary bypass to above the lower limit of autoregulation led to a reduction of postoperative delirium and improved memory 1 month after surgery compared with usual care. Together, the growing body of evidence suggests that monitoring CBF autoregulation provides prognostic information on eventual patient outcomes and offers potential for therapeutic intervention. For surgical patients, personalised blood pressure management based on CBF autoregulation data holds promise as a strategy to improve patient neurocognitive outcomes. [ABSTRACT FROM AUTHOR]

The early identification of a defect that is developing in a bearing is crucial for avoiding failures in rotating machinery. Frequency domain analysis of the vibration signals has been shown to contribute to a better understanding of the nature of a developing defect. Early signs of degradation might be more noticeable in certain frequency bands. The advantages in identifying and monitoring these bandwidths are several: prevention of serious machinery damages, reduction of the loss of investments, and improvement of the accuracy in failure predicting models. This paper presents and compares two approaches for the diagnosis of bearing faults. The first approach was knowledge-based. It relied on principles of mechanics to interpret the measured vibration signals and utilized prior knowledge of the bearing characteristics and testing parameters. The second approach was data-driven whereby data were acquired exclusively from the vibration signal. Both approaches were successfully applied for fault diagnosis by identifying the frequencies of the vibration spectra characteristic for the bearing under study. From this, bandwidths of interest for early fault detection could be determined. The diagnostic abilities of both approaches were studied to analyze and compare their individual strengths regarding the aspects of implementation time, domain knowledge, data processing associated knowledge, data requirements, diagnostic performance, and practical applicability. The advantages, apparent limitations as well as avenues for further improvement of both approaches are discussed. [ABSTRACT FROM AUTHOR]

The improvement of battery management systems (BMSs) requires the incorporation of advanced battery status detection technologies to facilitate early warnings of abnormal conditions. In this study, acoustic data from batteries under two discharge rates, 0.5 C and 3 C, were collected using a specially designed battery acoustic test system. By analyzing selected acoustic parameters in the time domain, the acoustic signals exhibited noticeable differences with the change in discharge current, highlighting the potential of acoustic signals for current anomaly detection. In the frequency domain analysis, distinct variations in the frequency domain parameters of the acoustic response signal were observed at different discharge currents. The identification of acoustic characteristic parameters demonstrates a robust capability to detect short-term high-current discharges, which reflects the sensitivity of the battery's internal structure to varying operational stresses. Acoustic emission (AE) technology, coupled with electrode measurements, effectively tracks unusually high discharge currents. The acoustic signals show a clear correlation with discharge currents, indicating that selecting key acoustic parameters can reveal the battery structure's response to high currents. This approach could serve as a crucial diagnostic tool for identifying battery abnormalities. [ABSTRACT FROM AUTHOR]

This work aims to define a strategy for maintaining a vertical posture of the human body under conditions of conflicting sensory stimuli using a method of trend change analysis. The investigations involved 28 healthy individuals (13 females, 15 males, average age = 21, SD = 1.3 years). Measurements were conducted with eyes opened and closed and in the virtual environment with two sceneries oscillating at two frequencies. Values in the time domain were calculated—the mean center of pressure (COP) velocity and movement range in the AP direction—as well as values based on the moving average convergence divergence (MACD) computational algorithm—the trend change index (TCI), MACD_dT, MACD_dS, and MACD_dV. After dividing the analysis into distinct time periods, an increase in TCI values was identified in the oscillating scenery at 0.7 and 1.4 Hz during the 0.5–1 and 0.2–0.5 s time periods, respectively. Statistically significant differences were observed between measurements with an oscillation frequency of 0.7 Hz and those with an oscillation frequency of 1.4 Hz during the 0.2–0.5 s and 0.5–1 s periods. The use of stock exchange indices in the assessment of the ability to keep a stable body posture supplements and extends standard analyses in the time and frequency domains. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the possible explanatory effect of geopolitical risks on the oil price changes from February 1986 to December 2019 by employing the nonlinear bivariate Granger causality test and frequency domain Granger causality test based on the geopolitical risk index. The results suggest that there exists a nonlinear causality from geopolitical risks to crude oil prices. Moreover, geopolitical risks have a short-term impact on oil prices, less than 12 months. Actual geopolitical events have a smaller and less lasting impact on oil prices than pure geopolitical risks. [ABSTRACT FROM AUTHOR]

This paper uses MATLAB software to demonstrate the performance of UWBSAR and conducts a comparative study with data obtained from conventional radar. It compares various antennas that support UWB, such as Vivaldi, MIMO, and monopole antennas, analyzed using SIMULINK. The paper discusses the design of UWBSAR to provide a comprehensive analytical picture of the processed images. The focus is on frequency domain analysis in general and the Range Migration Algorithm (RMA) in particular. The data obtained after signal processing is recorded to estimate the crossrange resolution, which is then compared with conventional SAR. The cross-range resolution estimated using UWBSAR is found to be lower than that of conventional radar, proving that UWBSAR is a better alternative for obtaining sharper images in short-range applications. High-quality images are reconstructed using a combination of UWB radar, SAR processing, and proposed algorithms to improve image quality. The investigation includes positive image generation to enhance sharpness and near-field imaging procedures. This paper also describes Ultra-Wideband (UWB) Synthetic Aperture Radar (SAR) and its application in operating at low frequencies to detect obscured targets beneath foliage. While it has obvious military applications, it also has civilian uses, such as in geophysical studies and weather forecasting. Several applications have been identified for both military and civilian environments. [ABSTRACT FROM AUTHOR]

The study is aimed at investigating the effects of nuclear fusion reactors, human development, and economic growth on nuclear energy consumption in the United States from 1990 to 2019 using time and frequency causality analyses. The time domain causality analysis examined the relationship between variables over time using a single test statistic, while the frequency domain analysis explored causality in the short and long term at different frequencies. The findings from the time domain analysis indicated that nuclear energy consumption had a unidirectional causal effect on the human development index. Conversely, nuclear fusion reactors had a unidirectional causal impact on nuclear energy consumption. The results from the frequency domain analysis revealed that economic growth had a permanent unidirectional causal effect on nuclear energy consumption. In contrast, nuclear energy consumption had a temporary unidirectional causal impact on the human development index. Additionally, there was a bidirectional temporary and permanent causal effect between nuclear fusion reactors and nuclear energy consumption. Based on these findings, the study recommends that the United States continue providing financial incentives to develop nuclear energy technologies, such as constructing new nuclear power plants and offering subsidies to encourage the use of nuclear energy. [ABSTRACT FROM AUTHOR]

Accelerometers play a crucial role in the railway industry, especially in track monitoring. Traditionally, they are placed on the railway tracks or often on bridges to monitor the health and condition of the infrastructure. Recently, there has been an increased focus on using regular trains to monitor the condition of railway infrastructure. Often, the sensors are placed based on certain assumptions without much scientific evidence or support. This paper utilizes the multibody simulation software GENSYS to identify the optimal placement of accelerometers on a passenger train for monitoring railway switch wear. Switch wear profiles were generated systematically and used as input for the simulations, studying acceleration at a total of 93 locations distributed among the wheelsets, bogies, and carbody. Based on both time and frequency domain analyses, optimal sensor locations were identified, generally close to the first bogie or wheelset at the leading carbody. Accelerations generated by the wheelset passing the switch can also be captured in the carbody, but it is important to note that these are several orders lower in magnitude compared to the acceleration on the wheelset. If accelerometers are to be placed in the carbody, correct sensitivity must be chosen, and a high-pass filter should be applied to capture the acceleration signals associated with switch wear. The study confirms that there is a direct correlation between the depth of switch wear and the magnitude of the acceleration. It remains effective even under various curve radii and train speeds. [ABSTRACT FROM AUTHOR]

Despite the key role of fibrosis in atrial fibrillation (AF), the effects of different spatial distributions and textures of fibrosis on wave propagation mechanisms in AF are not fully understood. To clarify these aspects, we performed a systematic computational study to assess fibrosis effects on the characteristics and stability of re-entrant waves in electrically-remodelled atrial tissues. A stochastic algorithm, which generated fibrotic distributions with controlled overall amount, average size, and orientation of fibrosis elements, was implemented on a monolayer spheric atrial model. 245 simulations were run at changing fibrosis parameters. The emerging propagation patterns were quantified in terms of rate, regularity, and coupling by frequency-domain analysis of correspondent synthetic bipolar electrograms. At the increase of fibrosis amount, the rate of reentrant waves significantly decreased and higher levels of regularity and coupling were observed (p < 0.0001). Higher spatial variability and pattern stochasticity over repetitions was observed for larger amount of fibrosis, especially in the presence of patchy and compact fibrosis. Overall, propagation slowing and organization led to higher stability of re-entrant waves. These results strengthen the evidence that the amount and spatial distribution of fibrosis concur in dictating re-entry dynamics in remodeled tissue and represent key factors in AF maintenance. [ABSTRACT FROM AUTHOR]

The stability of offshore floating wind turbine foundation platforms is a fundamental requirement for the efficiency and safety of wind power generation systems. This paper proposes a novel small-diameter float-type semi-submersible platform to improve system stability. To evaluate the superior motion stability of the proposed floating platform, a comprehensive frequency–domain response analysis and experimental study were conducted in comparison with the OC4-DeepCwind platform developed by the National Renewable Energy Laboratory (NREL). The respective comparison of the frequency–domain response analysis and the experimental results demonstrated that the proposed floating wind turbine platform shows better hydrodynamic characteristics and resonance avoidance capability. This not only reduces the Response Amplitude Operators (RAOs), but also enhances the system stability, namely, effectively avoiding the regions of concentrated wave loading and low-frequency ranges. Furthermore, the proposed small-diameter semi-submersible platform has the potential to reduce manufacturing costs, providing valuable insights for the manufacturing of offshore floating wind turbine systems. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a chronic degenerative joint disease causing limited mobility and pain, with no curative treatment available. Recent in vivo studies suggested autonomic alterations during OA progression in patients, yet clinical evidence is scarce. Therefore, autonomic tone was analyzed in OA patients via heart rate variability (HRV) measurements. Methods: Time-domain (SDRR, RMSSD, pRR50) and frequency-domain (LF, HF, LF/HF) HRV indices were determined to quantify sympathetic and parasympathetic activities. In addition, perceived stress, WOMAC pain as well as serum catecholamines, cortisol and dehydroepiandrosterone-sulphate (DHEA-S) were analyzed. The impact of the grade of disease (GoD) was evaluated by linear regression analysis and correlations with clinical data were performed. Results: GoD significantly impacted the autonomic tone in OA patients. All time-domain parameters reflected slightly decreased HRV in early OA patients and significantly reduced HRV in late OA patients. Moreover, frequency-domain analysis revealed decreased HF and LF power in all OA patients, reflecting diminished parasympathetic and sympathetic activities. However, LF/HF ratio was significantly higher in early OA patients compared to late OA patients and implied a clear sympathetic dominance. Furthermore, OA patients perceived significantly higher chronic stress and WOMAC pain levels compared to healthy controls. Serum cortisol and cortisol/DHEA-S ratio significantly increased with GoD and positively correlated with WOMAC pain. In contrast, serum catecholamines only trended to increase with GoD and pain level. Conclusions: This prospective study provides compelling evidence of an autonomic dysfunction with indirect sympathetic dominance in early and late knee OA patients for the first time based on HRV analyses and further confirmed by serum stress hormone measurements. Increased sympathetic activity and chronic low-grade inflammation in OA as well as in its major comorbidities reinforce each other and might therefore create a vicious cycle. The observed autonomic alterations coupled with increased stress and pain levels highlight the potential of HRV as a prognostic marker. In addition, modulation of autonomic activity represents an attractive future therapeutic option. [ABSTRACT FROM AUTHOR]

Introduction: In patients with idiopathic normal pressure hydrocephalus (iNPH), the characteristics of balance disturbance are not as well understood as those related to gait. This study examined changes in postural stability in quiet standing after the cerebrospinal fluid tap test (CSFTT) in these patients. Furthermore, the study explored the relationship between the amount of spontaneous body sway and both gait and executive function. Materials and methods: All patients diagnosed with iNPH underwent CSFTT. We evaluated their center of pressure (COP) measurements on a force plate during quiet standing, both pre- and post-CSFTT. Following the COP measurements, we calculated COP parameters using time and frequency domain analysis and assessed changes in these parameters after CSFTT. At pre-CSFTT, we assessed the Timed Up and Go (TUG) and the Frontal Assessment Battery (FAB). We investigated the relationship between COP parameters and the TUG and FAB scores at pre-CSFTT. Results: A total of 72 patients with iNPH were initially enrolled, and 56 patients who responded positively to CSFTT were finally included. Post-CSFTT, significant improvements were observed in COP parameters through time domain analysis. These included the velocity of COP (vCOP), root-mean-square of COP (rmsCOP), turn index, torque, and base of support (BOS), compared to the pre-CSFTT values (p<0.05). In the frequency domain analysis of COP parameters post-CSFTT, there was a decrease in both the peak and average of power spectral density (PSD) values in both the anteroposterior (AP) and mediolateral (ML) directions below 0.5 Hz (p<0.05). In addition, the TUG scores showed a positive correlation with vCOP, rmsCOP, turn index, torque, BOS, and both the peak and average PSD values in the AP and ML directions below 0.5 Hz (p < 0.05). The FAB scores demonstrated a negative correlation with vCOP, rmsCOP, turns index, BOS, and both peak and average PSD values in the AP direction below 0.5 Hz (p < 0.05). Conclusion: In patients with iNPH who responded to CSFTT, there was an improvement in spontaneous body sway during quiet standing after CSFTT. Increased spontaneous sway is associated with impaired gait and frontal lobe function. This may be linked to impaired cortico-cortical and cortico-subcortical circuits in patients with iNPH. [ABSTRACT FROM AUTHOR]

A jointed rock mass (JRM) is the usual case in practical engineering, which has significant effects on its mechanical performance. To clarify the difference in the seismic responses of underground structures in JRM sites or homogeneous rock mass (HRM) sites, two models were prepared to take shaking table tests in a structural laboratory. The HRM site was prepared following the similitude relations of material; meanwhile, underground structures of a metro station were embedded during the casting of the models. The JRM site and structure were made with the same material but produced random joints after the natural drying process. Different frequencies of harmonics were used to excite along the two models in the transverse or the longitudinal direction, respectively. The dynamic effect was evaluated by time-frequency and frequency-domain analyses. The test results compared with the HRM model indicated that the JRM model had a 22% reduction in the transverse fundamental frequency, but the dynamic response of the ground surface was enhanced due to the effect of the joints. Under harmonic excitations of the same intensity, the JRM model produced a greater energy response to the station structure and reduced the acceleration response of the platform in the high-frequency region. Meanwhile, the JRM model produced a peak tensile strain at the connections of the main and subsidiary structures that was 31% larger than that of the HRM model, and the range of tensile strains observed at the platform connecting the horizontal passage was 1.5 times larger than that of the HRM model. [ABSTRACT FROM AUTHOR]