Search

Determining the creep function of viscoelastic pipes is one of the challenges of modeling these pipes to calibrate or determine defects. The present research aims to determine the creep function of viscoelastic pipes using transient flow pressure in the time and frequency domains. For this purpose, the proposed method is first implemented using a numerical example. The numerical part investigated the effect of signal sample size, the number of Kelvin-Voigt (K-V) elements, repeatability, and decision variables. Then, using an experimental test, the desired methodology has been evaluated. In this research, the K-V mechanical model was used to define the creep function, and its parameters, including elastic pressure wave speed, retardation times, and creep complaint coefficients, were calibrated. The results showed that using pressure signals in both time and frequency domains provides stable results for the investigated pipeline. Examining the effect of signal size showed that the creep function can be estimated with reasonable accuracy in the time domain with a few initial cycles. Also, 14.33 dimensionless frequency for a simple reservoir-pipe-valve system can provide accurate results in the frequency domain. The results of this research can be used as a suitable pre-processing to reduce the dimensions of inputs in models based on artificial intelligence. [ABSTRACT FROM AUTHOR]

Due to the importance security of sensitive speech and audio signals such as the countries president's call phone, this research work presents an efficient audio cryptosystem. Audio speech communications play main and essential role in the human daily life, covering many important fields ranging from simple personal private information up to top secret, sensitive and critical information. It is difficult to prevent the eavesdropping by unauthorized person especially 1-D audio scrambling methods, it is built by simple algorithms and has low security level. So, there is a dire need for enhancing audio cryptosystem to be more secure against different types of attacks. In this work, the enhanced audio cryptosystem is proposed, the first approach enhances multiple key audio cryptosystem utilizing the chaotic baker map through enhancing the confusion level for both encryption/decryption algorithms employing time and transform domains. Simulation experiments revealed that the proposed boosted audio cryptosystem on time and transform domains is secure and robust against different types of attacks. The boosted multiple key based audio crypto-system is performed by increasing confusion and enlarging the secret-key space. Results of the simulation experiments revealed that the proposed multiple secret key-enhanced confusion audio cryptosystem is secure and robust with respect to different types of attacks. Moreover, results for transform domains show better results than results for time domain for the three proposed boosted audio cryptosystem. Also, the third proposed audio cryptosystem DWT-based shows the best results among all other proposed audio cryptosystem. Due to the results, robustness and reliability of proposed technique are higher the existing audio cryptographic system. [ABSTRACT FROM AUTHOR]

Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based approaches, particularly in sound field reconstruction. Physics-informed neural networks (PINNs) have emerged as a novel framework, bridging the gap between data-driven and model-based techniques for addressing physical phenomena governed by partial differential equations. This paper introduces a PINN-based approach for the recovery of arbitrary volumetric acoustic fields. The network incorporates the wave equation to impose a regularization on signal reconstruction in the time domain. This methodology enables the network to learn the physical law of sound propagation and allows for the complete characterization of the sound field based on a limited set of observations. The proposed method's efficacy is validated through experiments involving speech signals in a real-world environment, considering varying numbers of available measurements. Moreover, a comparative analysis is undertaken against state-of-the-art frequency domain and time domain reconstruction methods from existing literature, highlighting the increased accuracy across the various measurement configurations. [ABSTRACT FROM AUTHOR]

The electroencephalogram (EEG) is a cornerstone tool for the diagnosis, management, and prognosis of selected patient populations. EEGs offer significant advantages such as high temporal resolution, real-time cortical function assessment, and bedside usability. The quantitative EEG (qEEG) added the possibility of long recordings being processed in a compressive manner, making EEG revision more efficient for experienced users, and more friendly for new ones. Recent advancements in commercially available software, such as Persyst, have significantly expanded and facilitated the use of qEEGs, marking the beginning of a new era in its application. As a result, there has been a notable increase in the practical, real-world utilization of qEEGs in recent years. This paper aims to provide an overview of the current applications of qEEGs in daily neurological emergencies and ICU practice, and some elementary principles of qEEGs using Persyst software in clinical settings. This article illustrates basic qEEG patterns encountered in critical care and adopts the new terminology proposed for spectrogram reporting. [ABSTRACT FROM AUTHOR]

There is an increasing need for compact low-cost NMR apparatus that can be used on the laboratory bench and in the field. There are four main usage variants of usage: (a) time-domain apparatus, particularly for physical measurements; (b) frequency-domain apparatus, particularly for chemical analysis, (c) NMR Cryoporometry apparatus for measuring pore-size distributions; and (d) MRI apparatus for imaging. For all of these, variable temperature capability may be vital. We have developed compact low-cost apparatus targeted at these applications. We discuss a hand-held NMR Spectrometer, and three different holdable NMR magnets, with sufficiently large internal bores for the Lab-Tools compact Peltier thermo-electric cooled variable-temperature probes. Currently, the NMR Spectrometer is very suitable for (a) NMR time-domain relaxation and (c) NMR Cryoporometry. With a suitable high-homogeneity magnet, it is also appropriate for simple use (b), spectral analysis, or, with a suitable gradient set, (d) MRI. Together, the NMR Spectrometer, one of the NMR variable-temperature probes, and any of these NMR magnets make excellent NMR Cryoporometers, as demonstrated by this paper and previously published research. Equally, they make versatile general-purpose variable-temperature NMR systems for materials science. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive collection of formulas and calculations for hand-crafted feature extraction of condition monitoring signals. The documented features include 123 for the time domain and 46 for the frequency domain. Furthermore, a machine learning-based methodology is presented to evaluate the performance of features in fault classification tasks using seven data sets of different rotating machines. The evaluation methodology involves using seven ranking methods to select the best ten hand-crafted features per method for each database, to be subsequently evaluated by three types of classifiers. This process is applied exhaustively by evaluation groups, combining our databases with an external benchmark. A summary table of the performance results of the classifiers is also presented, including the percentage of classification and the number of features required to achieve that value. Through graphic resources, it has been possible to show the prevalence of certain features over others, how they are associated with the database, and the order of importance assigned by the ranking methods. In the same way, finding which features have the highest appearance percentages for each database in all experiments has been possible. The results suggest that hand-crafted feature extraction is an effective technique with low computational cost and high interpretability for fault identification and diagnosis. [ABSTRACT FROM AUTHOR]

This work investigates the interaction between water waves and multiple ice shelf fragments in front of a semi-infinite ice sheet. The hydrodynamics are modelled using shallow water wave theory and the ice shelf vibration is modelled using Euler–Bernoulli beam theory. The ensuing multiple scattering problem is solved in the frequency domain using the transfer matrix method. The appropriate conservation of energy identity is derived in order to validate our numerical calculations. The transient scattering problem for incident wave packets is constructed from the frequency domain solutions. By incorporating multiple scattering, this paper extends previous models that have only considered a continuous semi-infinite ice shelf. This paper serves as a fundamental step towards developing a comprehensive model to simulate the breakup of ice shelves. [ABSTRACT FROM AUTHOR]

There is an increasing need for compact low-cost NMR apparatus that can be used on the laboratory bench and in the field. There are four main usage variants of usage: (a) time-domain apparatus, particularly for physical measurements; (b) frequency-domain apparatus, particularly for chemical analysis, (c) NMR Cryoporometry apparatus for measuring pore-size distributions; and (d) MRI apparatus for imaging. For all of these, variable temperature capability may be vital. We have developed compact low-cost apparatus targeted at these applications. We discuss a hand-held NMR Spectrometer, and three different holdable NMR magnets, with sufficiently large internal bores for the Lab-Tools compact Peltier thermo-electric cooled variable-temperature probes. Currently, the NMR Spectrometer is very suitable for (a) NMR time-domain relaxation and (c) NMR Cryoporometry. With a suitable high-homogeneity magnet, it is also appropriate for simple use (b), spectral analysis, or, with a suitable gradient set, (d) MRI. Together, the NMR Spectrometer, one of the NMR variable-temperature probes, and any of these NMR magnets make excellent NMR Cryoporometers, as demonstrated by this paper and previously published research. Equally, they make versatile general-purpose variable-temperature NMR systems for materials science.

Abstract Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based approaches, particularly in sound field reconstruction. Physics-informed neural networks (PINNs) have emerged as a novel framework, bridging the gap between data-driven and model-based techniques for addressing physical phenomena governed by partial differential equations. This paper introduces a PINN-based approach for the recovery of arbitrary volumetric acoustic fields. The network incorporates the wave equation to impose a regularization on signal reconstruction in the time domain. This methodology enables the network to learn the physical law of sound propagation and allows for the complete characterization of the sound field based on a limited set of observations. The proposed method’s efficacy is validated through experiments involving speech signals in a real-world environment, considering varying numbers of available measurements. Moreover, a comparative analysis is undertaken against state-of-the-art frequency domain and time domain reconstruction methods from existing literature, highlighting the increased accuracy across the various measurement configurations.

We present a novel multi-modal adaptive feature extraction algorithm considering both time-domain and frequency-domain modalities (AFETF), coupled with a Bidirectional Long Short-Term Memory (Bi-LSTM) network based on the Grey Wolf Optimizer (GWO) for early-stage weak fault diagnosis in bearings. Singular Value Decomposition (SVD) was employed for noise reduction, while Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was utilized for signal decomposition, facilitating further signal processing. AFETF algorithm proposed in this paper was employed to extract weak fault features. The adaptive diagnostic process was further enhanced using Bi-LSTM network optimized with GWO, ensuring objectivity in the hyperparameter optimization. The proposed method was validated for datasets containing weak faults with a 0.2 mm crack and strong faults with a 0.4 mm crack, demonstrating its effectiveness in early-stage fault detection.

Abstract The accurate classification of road surface conditions plays a vital role in ensuring road safety and effective maintenance. Vibration-based techniques have shown promise in this domain, leveraging the unique vibration signatures generated by vehicles to identify different road conditions. In this study, we focus on utilizing vehicle-mounted vibration sensors to collect road surface vibrations and comparing various data representation techniques for classifying road surface conditions into four classes: normal road surface, potholes, bad road surface, and speedbumps. Our experimental results reveal that the combination of multiple data representation techniques results in higher performance, with an average accuracy of 93.4%. This suggests that the integration of deep neural networks and signal processing techniques can produce a high-level representation better suited for challenging multivariate time series classification issues.

Over the past 10 years, deep learning has enabled significant advancements in the improvement of noisy speech. In an end-to-end speech enhancement, the deep neural networks transform a noisy speech signal to a clean speech signal in the time domain directly without any conversion or estimation of mask. Recently, the U-Net-based models achieved good enhancement performance. Despite this, some of them may neglect context-related information and detailed features of input speech in case of ordinary convolution. To address the above issues, recent studies have upgraded the performance of the model by adding various network modules such as attention mechanisms, long and short-term memory (LSTM). In this work, we propose a new U-Net-based speech enhancement model using a novel lightweight and efficient Shuffle Attention (SA), Gated Recurrent Unit (GRU), residual blocks with dilated convolutions. Residual block will be followed by a multi-scale convolution block (MSCB). The proposed hybrid structure enables the temporal context aggregation in time domain. The advantage of shuffle attention mechanism is that the channel and spatial attention are carried out simultaneously for each sub-feature in order to prevent potential noises while also highlighting the proper semantic feature areas by combining the same features from all locations. MSCB is employed for extracting rich temporal features. To represent the correlation between neighboring noisy speech frames, a two Layer GRU is added in the bottleneck of U-Net. The experimental findings demonstrate that the proposed model outperformed the other existing models in terms of short-time objective intelligibility (STOI), and perceptual evaluation of the speech quality (PESQ). [ABSTRACT FROM AUTHOR]

Facial expressions are often considered the primary indicators of emotions. However, it is challenging to detect genuine emotions because they can be controlled. Many studies on emotion recognition have been conducted actively in recent years. In this study, we designed a convolutional neural network (CNN) model and proposed an algorithm that combines the analysis of bio-signals with facial expression templates to effectively predict emotional states. We utilized the EfficientNet-B0 architecture for network design and validation, known for achieving maximum performance with minimal parameters. The accuracy for emotion recognition using facial expression images alone was 74%, while the accuracy for emotion recognition combining biological signals reached 88.2%. These results demonstrate that integrating these two types of data leads to significantly improved accuracy. By combining the image and bio-signals captured in facial expressions, our model offers a more comprehensive and accurate understanding of emotional states. [ABSTRACT FROM AUTHOR]

This paper provides the relationship between Economic Policy Uncertainty (EPU) and international tourism activities in the BRIC countries (i.e. Brazil, Russia, India and China). The researchers used continuous wavelets, wavelet coherence and wavelet phase-difference based on Granger causality analysis to investigate the relationship between EPU and tourism activities using the annual data. The findings show that there is a unidirectional causal influence of EPU on international tourism arrivals (ITA) in the short run and mid run for Brazil. In addition, there is a bi-directional causal influence of EPU on ITA in China and Russia in the long run. [ABSTRACT FROM AUTHOR]

In this study, a numerical analysis is carried out to investigate the coupled behaviour of a TLP (tension leg platform) -TAD (tender-assisted drilling) system under a series of regular waves in surge direction. The numerical model is established in the time domain considering the nonlinear characteristics of the mooring systems and the hydrodynamic interference. After the case validations, the effects of the wave periods and amplitudes on the nonlinear dynamic characteristics of the surge motions are investigated in detail. Then, the observed nonlinear dynamic characteristics are interpreted in detail through the existing theory of phase dynamics. The surge motions of the coupled system can show multiperiod nonlinear characteristics under regular waves in certain wave periods. Three forms of phase dynamic characteristics: phase locking, phase trapping, and phase drifting and slipping are observed in the surge motions of the tender-assisted drilling operation system. [ABSTRACT FROM AUTHOR]

Optical timing with rapid, seconds-to-minutes cadences with high photometric precision and gap-free long baselines is necessary for an unambiguous physical picture of accretion phenomena, and is only possible from space. Exoplanet-hunting missions like Kepler and TESS have offered an outstanding new window into detailed jet and accretion physics, but have been severely hampered by incomplete calibration and systematics treatments and, most especially, a monochromatic single wide bandpass. Advances made using Kepler and TESS survey data, when considered alongside detailed, expensive multi-color experiments done from the ground, reveal the enormous potential of a space-based multi-color optical timing mission with a high energy focus. [ABSTRACT FROM AUTHOR]

EET (equality equivalence test) non‑parameter method was proposed to examine people’s distributional preferences for time. Firstly, time distributional preferences were classified into nine types by utilizing the EET method, assumptions were introduced to reflect the unique features in the time domain. Then, based on the two rounds of behavioral experiments conducted in both time and money domains, decision makers’ preference types were identified by the indifference curves, and the pattern was analyzed. The results showed that: relative to money, more people are influenced by others’ payoff when exposed to time distribution problems. As people use heuristic thinking due to the infungibility of time, their own payoff resultingly converges to the payoffs of others; people tend to be more benevolent when they have greater payoff relative to their counterparts, and more malevolent when having relatively less payoff; no extreme forms of spite are detected. [ABSTRACT FROM AUTHOR]

In engineering applications, the loads acting on engineering structures are mostly distributed loads, so distributed load identification technology has always attracted much attention. The dynamic calibration technology identifies the load by establishing the relationship between the load and the response, and the traditional dynamic calibration method obtains the calibration matrix according to the finite element simulation, and the recognition results are affected by the model error. Based on the Gauss-Legendre integral, the infinite-dimensional dynamic calibration is converted into the finite-dimensional dynamic calibration, the time-domain indexing matrix is obtained through inverse Fourier transform and convolutional integration, the time-domain identification of the distributed load applied to the structure is carried out, and the effectiveness and high accuracy of the proposed method are verified by simulation. The noise is added to the response to test the noise resistance of the method, and compared with the traditional simulation calibration method. And the influence of the total number of Gaussian points on the recognition accuracy is discussed. [ABSTRACT FROM AUTHOR]

We present a novel multi-modal adaptive feature extraction algorithm considering both time-domain and frequency-domain modalities (AFETF), coupled with a Bidirectional Long Short-Term Memory (Bi-LSTM) network based on the Grey Wolf Optimizer (GWO) for early-stage weak fault diagnosis in bearings. Singular Value Decomposition (SVD) was employed for noise reduction, while Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was utilized for signal decomposition, facilitating further signal processing. AFETF algorithm proposed in this paper was employed to extract weak fault features. The adaptive diagnostic process was further enhanced using Bi-LSTM network optimized with GWO, ensuring objectivity in the hyperparameter optimization. The proposed method was validated for datasets containing weak faults with a 0.2 mm crack and strong faults with a 0.4 mm crack, demonstrating its effectiveness in early-stage fault detection. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for identifying distributed dynamic loads in the time domain. Using polynomial andmodal analysis, the load is transformed intomodal space for coefficient identification. This allows the distributed dynamic load with a two-dimensional form in terms of time and space to be simultaneously identified in the form of modal force, thereby achieving dimensionality reduction. The Impulse-based Force Estimation Algorithm is proposed to identify dynamic loads in the time domain. Firstly, the algorithm establishes a recursion scheme based on convolution integral, enabling it to identify loads with a long history and rapidly changing forms over time. Secondly, the algorithm introduces moving mean and polynomial fitting to detrend, enhancing its applicability in load estimation. The aforementioned methodology successfully accomplishes the reconstruction of distributed, instead of centralized, dynamic loads on the continuum in the time domain by utilizing acceleration response. To validate the effectiveness of the method, computational and experimental verification were conducted. [ABSTRACT FROM AUTHOR]

The accurate classification of road surface conditions plays a vital role in ensuring road safety and effective maintenance. Vibration-based techniques have shown promise in this domain, leveraging the unique vibration signatures generated by vehicles to identify different road conditions. In this study, we focus on utilizing vehicle-mounted vibration sensors to collect road surface vibrations and comparing various data representation techniques for classifying road surface conditions into four classes: normal road surface, potholes, bad road surface, and speedbumps. Our experimental results reveal that the combination of multiple data representation techniques results in higher performance, with an average accuracy of 93.4%. This suggests that the integration of deep neural networks and signal processing techniques can produce a high-level representation better suited for challenging multivariate time series classification issues. [ABSTRACT FROM AUTHOR]

Induction motors (IM) play a fundamental role in the industrial sector because they are robust, efficient, and low-cost machines. Changes in the environment, installation errors, or modifications to working conditions can generate faults in induction motors. The trend on IM fault detection is focused on the design techniques and sensors capable of evaluating multiple faults with various signals using non-invasive analysis. The methodology is based on processing electric current signals by applying the short-time Fourier transform (STFT). Additionally, the computation of the mean and standard deviation of infrared thermograms is proposed as main indicators. The proposed system combines both parameters by means of Support Vector Machine and k-nearest-neighbor classifiers. The development of the diagnostic system was done with digital hardware implementations using a Xilinx PYNQ Z2 card that integrates an FPGA with a microprocessor, thus taking advantage of the acquisition and processing of digital signals and images in hardware. The proposed method has proved to be effective for the classification of healthy (HLT), misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB) faults with an accuracy close to 99%. [ABSTRACT FROM AUTHOR]

Curved panels are used for ships due to complex situations that can be under external loading. So, it is very important to know about the stability related to the curved system under Low-Velocity Impact (LVI). To improve the stability of this kind of structure, graphene oxide powders (GOPs) are added to its matrix. In addition, the contact force between the impactor and shell is evaluated by implementing Hertz contact theory. So, in the current work, as a first attempt, energy absorption and low-velocity impact response of functionally graded graphene oxide powders-reinforced nanocomposite panel on the viscoelastic substrate is presented. Higher-order shear deformation theory (HSDT), which has twelve factors, is employed to determine the displacement domains. Also, the minimum potential energy technique is designated to obtain the governing equations and end conditions. Moreover, Galerkin and Newmark methods are chosen to solve the relationships. The governing equations related to the curved system under external excitation aimed at achieving the LVI and dynamic outputs are determined by the connection of Galerkin and Newmark methods. The uniqueness of this research is considering the impacts associated with HSDT and various Boundary Conditions (BCs) in addition to considering FG oxide powder reinforcement. The outputs segment displays the effects related to the BCs, the radius of the impactor, mass, and velocity, as well as the weight fraction related to the GOPs on contact force, the indentation, and the indenter velocity. [ABSTRACT FROM AUTHOR]

Bayan Obo is a well-known polymetallic deposit containing significant quantities of rare earth elements, niobium, thorium, and iron. However, the epoch in which mineralization occurred and the mineralization process are still debated due to the complex nature of its mineralization and geological evolution. Inadequate geophysical exploration has further contributed to this lack of clarity surrounding critical issues, such as the deep link between the main orebody and the eastern orebody, the form and distribution of the extensive dolomite, and the geologic structures in the area. Therefore, we implemented the time-domain controlled-source electromagnetic method (CSEM) to acquire electrical structures at depths down to 2.5 km between the Main and Eastern mines. According to the inverted resistivity structure, in conjunction with existing geological and drilling data, we classified the main lithologies and faults based on their resistivity characteristics. Overall, the mineralized carbonatite reflects high to moderately high resistivity. The mineralized carbonatite dips overall from north to south, with a maximum extension depth not exceeding 1.5 km, and its range of occurrence is controlled by nearly east–west-striking faults distributed along the bounding line between the roof and floor rocks. The Main and Eastern mines are connected at depth, but the morphology and position of the ore bodies have significantly changed due to multiple phases of tectonic activity. The electrical structure does not reveal any obvious syncline structures, further refuting the traditional view that the Bayan syncline controls ore formation. [ABSTRACT FROM AUTHOR]

This paper is devoted to establishing the resonant modal expansion of the lowfrequency part of the scattered field for acoustic bubbles embedded in elastic materials. Due to the nanobubble with damping, the Minnaert resonance can be induced at certain discrete resonant frequencies, which forms the fundamental basis of effectively constructing elastic metamaterials via the composite material theory. There are two major contributions in this work. First, we ansatz a special form of the density, approximate the incident field with a finite number of modes, and then obtain an expansion with a finite number of modes for the acoustic-elastic wave scattering in the time-harmonic regime. Second, we show that the low-frequency part of the scattered field in the time domain can be well approximated by using the modal expansion with sharp error estimates. Interestingly, we find that the 0th mode is the main contribution to reconstruct the information of the low-frequency part of the scattered field. [ABSTRACT FROM AUTHOR]

Aiming at the problem that speech enhancement networks have difficulty in extracting global speech-related features and are ineffective in capturing local contextual information of speech. This paper proposed a two-branch attention and U-Net-based time-domain speech enhancement method, which used a U-Net encoder-decoder structure and took the high-dimensional time-domain features obtained from a single-channel noisy speech after one-dimensional convolution as input. Firstly, this paper designed Conformer-based residual convolution to enhance the noise reduction ability of network by utilizing residual connection. Secondly, this paper designed a two-branch attention mechanism structure, which utilized global and local attention to obtain richer contextual information in the noisy speech, and at the same time, to effectively represent the long sequence features and extract more diverse feature information. Finally, this paper constructed a weighted loss function by combining the loss function in the time domain and frequency domain to train the network and improve the performance in speech enhancement. This paper used several metrics to evaluate the quality and intelligibility of the enhanced speech, the enhanced speech perceptual evaluation of speech quality(PESQ) on the public datasets Voice Bank+DEMAND is 3.11, the short-time objective intelligibility(STOI) is 95%, the composite measure for predicting signal rating(CSIG) is 4.44, the composite measure for predicting background noise(CBAK) is 3.60, and the composite measure for predicting overall processed speech quality(COVL) is 3.81, in which the PESQ is improved by 7.6% compared to SE-Conformer, and improved by 5.1% compared to TSTNN improved by 5.1%. Experimental results show that the proposed method achieves better results in various metrics of speech denoising and meets the requirements for speech enhancement tasks. [ABSTRACT FROM AUTHOR]

The study of electroencephalographic (EEG) signals has gained popularity in recent years because they are unlikely to intentionally fake brain activity. However, the reliability of the results is still subject to various noise sources and potential inaccuracies inherent to the acquisition process. Analyzing these signals involves three main processes: feature extraction, feature selection, and classification. The present study extensively evaluates feature sets across domains and their impact on emotion recognition. Feature selection improves results across the different domains. Additionally, hybrid models combining features from various domains offer a superior performance when applying the public DEAP dataset for emotion classification using EEG signals. Time, frequency, time–frequency, and spatial domain attributes and their combinations were analyzed. The effectiveness of the input vectors for the classifiers was validated using SVM, KNN, and ANN, which are simple classification algorithms selected for their widespread use and better performance in the state of the art. The use of simple machine learning algorithms makes the findings particularly valuable for real-time emotion recognition applications where the computational resources and processing time are often limited. After the analysis stage, feature vector combinations were proposed to identify emotions in four quadrants of the valence–arousal representation space using the DEAP dataset. This research achieved a classification accuracy of 96% using hybrid features in the four domains and the ANN classifier. A lower computational cost was obtained in the frequency domain. [ABSTRACT FROM AUTHOR]

This study analyzes the dynamic connectedness (i.e., spillovers and spillbacks) of financial stress across advanced and emerging economies. As proxy for financial stress, we reconstruct the financial stress index (FSI) for 16 advanced economies and 15 emerging economies from January 1997 to August 2020. The constructed FSIs reflect combined stress level in banking sectors, equity markets, capital markets and exchange rate markets. Using frameworks proposed by Diebold and Yilmaz (Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66) and Baruník and Křehlík (Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271–296), we find that there is strong connectedness of financial stress across economies. Moreover, the connectedness of the financial stress is stronger after the global financial crisis and during the COVID-19 pandemic. Although the spillover of shocks is strongest in the short-term horizon, the spillovers in the longer-term horizons are not trivial. Our results also show that the US is the largest shock transmitter as well as one of the largest shock receivers. Our results also suggest that shocks originating in advanced economies have strong effects on other economies, but shocks originating in emerging economies also play an increasing role. Global factors such as global economic policy uncertainty and geopolitical risks influence the magnitude of the spillover of financial stress. [ABSTRACT FROM AUTHOR]

In this work, we present a straightforward and simple method of moments (MOM) solution procedure, with minimum mathematical manipulations, to solve the coupled integral equations for multiple, homogeneous and inhomogeneous, dielectric bodies of arbitrary shape directly in the time domain. The standard surface and volume integral equation formulations are used for homogeneous and inhomogeneous bodies, respectively. The numerical solution procedure does not involve a time-marching process as is usually adopted for time domain problems and seems to be one of the primary reasons for the late-time instabilities as a result of error accumulation. The present solution method is stable for a very long time as evidenced by several representative numerical examples presented for validation. [ABSTRACT FROM AUTHOR]

In industrial applications, rotating machines operate under real-time variable speed and load regimes. In the presence of faults, the degradation of critical components is accelerated significantly. Therefore, robust monitoring algorithms able to identify these faults become crucial. In the literature, it is hard to find comprehensive monitoring systems that include variable speed and load regimes with combined gearbox faults using electrical and vibration signals. For this purpose, a novel signal processing methodology including a geometric classification technique is proposed. This methodology is based on using different types of sensors such as current, voltage and vibration sensors with a regime normalization, which allows the grouping of different regimes belonging to the same health state. It consists of reducing dispersion between the class observations and separating other classes representing different health states including the variation in speed and load. Then, a peripheral threshold is proposed in our classifier to diagnose new health states. To verify the effectiveness of the methodology, current, voltage and vibration data from a gearbox system are collected under variable speed and load levels. [ABSTRACT FROM AUTHOR]

The letter presents a method of frequency to time domain conversion of Scattering Parameter data for microwave imaging applications. The time to frequency domain conversion was initially tested in the MATLAB. The testing was conducted in a realistic microwave imaging environment with an antenna and a heterogeneous breast phantom. Note that, the time domain signal obtained from the Vector Network Analyzer (VNA) as well as the time domain signal obtained with the introduced process is similar indicating efficiency of the proposed method. An ultra‐wideband (UWB) signal is transmitted from the transmitting Antipodal Vivaldi Antenna to the breast phantom and the received signals are collected by the same antenna over a monostatic process. A total of 36 antenna rotations were considered while scanning the phantom from various orientations. All the data stored in the VNA is extracted and the proposed frequency to time domain conversion technique was applied to it. Finally, the Delay and Sum beamforming algoirithm was used to reconstruct the target image. [ABSTRACT FROM AUTHOR]

This study introduces an all-digital CMOS time-domain smart temperature sensor (STS) that offers a smaller circuit area and reduced complexity. In contrast to previous studies that utilized multiple delay lines or additional path selection circuits, the proposed new structure employs a single cyclic path and fewer delay lines. The functionality of high-resolution temperature sensing, pulse-shrinking time measurement, and built-in offset-error cancellation is achieved using only one cyclic delay line (CDL). The temperature-sensing delay line generates a thermal-dependent pulse width proportional to the absolute temperature (PTAT). Subsequently, a pulse-shrinking unit, implemented within the pulse-shrinking delay line, performs time-to-digital conversion by measuring the PTAT pulse width. Finally, a time-added delay line within the CDL incorporates a simple D-type Flip Flop to enable concise offset-error cancellation, thereby improving accuracy. This study further simplifies the cancellation circuitry to reduce the overall circuit area. The proposed sensor, fabricated using a TSMC 0.35 μ m CMOS process, occupies an area of 0.022 mm 2 , providing a cost-effective solution for pulse-shrinking STSs. The maximum inaccuracy after offset-error cancellation is 1. 3 ∘ C within a temperature range of 0 ∼ 80 ∘ C, with a high resolution of approximately 0. 035 ∘ C/LSB. This resolution enhancement significantly surpasses similar studies. By reducing circuit complexity, the proposed sensor successfully achieves improvements in both area and resolution. [ABSTRACT FROM AUTHOR]

This document, published in the journal Clinical Autonomic Research, explores the relationship between a specific gene polymorphism (rs1801253) and cardiac sympathetic modulation as assessed by spectral analysis of heart rate variability (HRV). The study involved 241 healthy volunteers and found that carriers of the Gly/Gly genotype in the ADRB1 gene had an imbalance in sympathetic and parasympathetic activity, which could potentially increase the risk for cardiovascular diseases. The study suggests that this gene polymorphism may be a plausible explanation for the contribution of genetics to HRV in healthy individuals. However, the study has limitations, such as a relatively small sample size and the analysis being limited to supine rest conditions. [Extracted from the article]

Copyright of Chinese Journal of Applied Mechanics is the property of Chinese Journal of Applied Mechanics 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.)

A simplified model is proposed for predicting the nonlinear dynamic response of vertically loaded tapered piles in the time domain, in which the tapered pile is divided into several frustum segments and the four-spring is used for the simulation of the soil–pile interaction. The differential equations for the tapered pile are given and solved by the finite difference method. The vertical dynamic response of a typical tapered pile is investigated, and the consistency of the computational results compared with the finite element results convincingly verifies the reliability of the proposed simplified model. Then, recommended segment numbers, considering the computational efficiency and accuracy requirements for the dynamic analysis of tapered piles, are given. And parametric studies are also carried out to investigate the effect of soil and pile parameters on the nonlinear dynamic response of the tapered pile. The results show that soil nonlinearity significantly affects the vertical dynamic characteristics of the tapered pile. And the tapered pile shows better dynamic characteristics than the cylindrical pile with the same volume and pile length. In addition, the properties of the soil along the upper part of the tapered pile have a more considerable effect on the dynamic response of the tapered pile. These results help to further improve the theory of nonlinear dynamic response analysis of tapered piles and promote its widespread application in engineering practice. [ABSTRACT FROM AUTHOR]